house price indices from the 1984â€“1992msa american housing surveys

House Price Indices from the 1984–1992 MSA American Housing Surveys 439Journal of Housing Research • Volume 6, Issue 3 439© Fannie Mae 1995. All Rights Reserved.

House Price Indices from the 1984–1992 MSA AmericanHousing Surveys

Thomas G. Thibodeau*

Abstract

This article reports residential real estate price indices computed from the Metropolitan Statis-tical Area American Housing Survey for 1984 through 1992. It extends the hedonic price indicesreported earlier to metropolitan areas surveyed during those years. Price indices for owner-occupied housing and for rental housing services are computed using 1985 national averagehousing characteristics. Housing inflation rates are measured using Laspeyres, Paasche, andFisher indices.

The article provides (1) house price indices based on random samples of the entire housing stockrather than (nonrandom) samples of properties that sell one or more times; (2) indices that pricea constant bundle of housing characteristics across the 44 metropolitan areas and over time;(3) indices for both house prices and housing inflation rates; (4) tenure-specific price indices fornew housing, existing standard-quality housing, and substandard housing; and (5) an estimate ofthe gradual improvements in the nation’s housing stock.

Keyword: hedonic house price indices

Introduction

Accurate measurement of house prices is important for a variety of reasons. Housingconsumers, urban economists, and housing policy analysts require information on houseprices when making housing consumption decisions, when modeling housing marketbehavior, and when evaluating the equity and efficiency implications of alternativegovernment housing assistance programs. Constant-quality metropolitan-area houseprices enable consumers to make comparisons across housing markets. Urban econo-mists use house price indices for many purposes:

1. To identify the determinants of spatial and temporal variation in house prices(Blackley and Follain 1987; Fortura and Kushner 1986; Guntermann and Norrbin1987; Manning 1989; Ozanne and Thibodeau 1983)

2. To measure the rate of economic depreciation for housing (Hulten and Wykoff 1981;Malpezzi, Ozanne, and Thibodeau 1980, 1987; Randolph 1988; Shilling, Sirmans,and Dombrow 1991)

* Thomas G. Thibodeau is Professor of Real Estate at the E. L. Cox School of Business, Southern MethodistUniversity. This research was supported by Fannie Mae, the Housing and Household Economic StatisticsDivision of the U.S. Bureau of the Census, and the Folsom Institute for Development and Land Use Policy atthe E. L. Cox School of Business at Southern Methodist University. The author thanks Daniel H. Weinbergfor providing access to the data and a pleasant working environment. The opinions expressed are those of theauthor and do not necessarily represent the views of Fannie Mae, the Folsom Institute, or the Bureau of theCensus. The author accepts responsibility for any errors in the article.

440 Thomas G. Thibodeau

3. To examine the influence of federal income taxes on tenure choice (Cooperstein1989; Cronin 1983; Grootaert and Dubois 1988; Herrin and Kern 1992; Lea andWasylenko 1983; Nicholson and Willis 1991; Woodward and Weicher 1989)

4. To measure property tax capitalization (Ihlanfeldt 1983; Ihlanfeldt and Boehm1983; Ihlanfeldt and Jackson 1982; King 1973)

5. To estimate models of housing search and household mobility (Boehm 1984; DeBoer1985)

6. To examine how households form their expectations of house value appreciation(Hamilton and Schwab 1985)

7. To measure housing inflation and rates of return on housing (Crone 1988; Kiel andCarson 1990; Manning 1986; Ozanne 1981; Pollakowski, Stegman, and Rohe 1991)

8. To measure housing quality (Wieand 1983)

9. To test for bias in homeowners’ estimates of house value (Follain and Malpezzi 1981)

10. To test for racial discrimination in the housing market (King and Mieszkowski 1973)

11. To quantify the influence of externalities on residential properties (Grether andMieszkowski 1974, 1980; Li and Brown 1980; Mieszkowski and Saper 1978; Thibodeau1990)

12. To evaluate the effect of alternative mortgage instruments on house prices (Agarwaland Phillips 1983, 1984)

Finally, housing policy analysts use house price indices to examine the efficiency ofgovernment housing assistance programs (Jackson and Mohr 1986; Olsen and Barton1983; Reeder 1985; Sa-Aadu 1984a, 1984b; Schwab 1985), to assess housing affordability(Linneman and Megbolugbe 1992), and to study how rent control affects housing markets(Marks 1984; Olsen 1972; Willis, Malpezzi, and Tipple 1990).

This article reports house price indices using data obtained from the 44 metropolitanstatistical areas (MSAs) surveyed in the MSA American Housing Survey (AHS) for 1984through 1992. Since 1984, the U.S. Bureau of the Census has conducted detailed surveysof the housing stock in 44 metropolitan areas. Metropolitan areas are surveyed in a 4-yearcycle, with 11 areas surveyed each year. Each metropolitan AHS uses a random sampleof about 3,200 residential dwellings. The data are collected between April and October ofthe survey year. Each survey questionnaire contains more than 300 questions ondwelling and occupant characteristics.

House price indices are computed by the hedonic index method. This statistical procedureuses regression analysis to explain variation in rents and house values using propertystructural and neighborhood characteristics (dwelling size, age, location, etc.). Separate

House Price Indices from the 1984–1992 MSA American Housing Surveys 441

regression equations are estimated for specified owner-occupied1 and for specifiedrenter-occupied2 properties in each of the 99 metropolitan surveys (9 survey years,11 metropolitan areas per year).

Tenure-specific house prices are computed by predicting the market rent or market valuefor a constant-quality dwelling. Separate averages are used to price dwelling character-istics for renter- and owner-occupied properties. For renter-occupied dwellings, thepredicted rent measures the price of one month of rental housing services. Because thehousing characteristics that are priced are held constant across all surveys, differencesin predicted rents reflect differences in the price of rental housing services. The predictedrents measure shelter rent by excluding utility payments. For owner-occupied units, thepredicted house value measures the price of a constant-quality house.

Within each metropolitan area, house price indices are computed for the entire housingstock as well as for three distinct points in the dwelling quality distribution: (1) forsubstandard housing (according to a definition of substandard housing previously usedby the U.S. Department of Housing and Urban Development [HUD]), (2) for new housing(housing less than three years old and not substandard), and (3) for existing standard-quality housing (everything else). The housing characteristics that are priced arenational average housing characteristics for dwellings in places with populations exceed-ing 100,000. Average dwelling characteristics were computed from the 1985 NationalAmerican Housing Survey, a random sample of the nation’s housing stock.

The hedonic specification and the housing markets examined here are similar to thoseused earlier. Thibodeau (1989, 1992) computed tenure-specific house price indices for60 metropolitan areas surveyed in the Standard Metropolitan Statistical Area AnnualHousing Survey between 1974 and 1983. Each metropolitan area was surveyed repeat-edly in a three- or four-year cycle, yielding 164 metropolitan surveys.

In 1984, the U.S. Bureau of the Census redesigned the metropolitan Annual HousingSurvey, renaming it the American Housing Survey and changing it in two significantways. First, the metropolitan-area coverage was reduced from 60 areas to 44: Sixteenmetropolitan areas were dropped from the survey, some metropolitan areas werecombined, and two metropolitan areas—San Jose, CA, and Tampa, FL—were added.Second, the Census Bureau extended the geographic boundaries for approximately two-thirds of the metropolitan areas, to incorporate suburban counties, and relabeled themmetropolitan statistical areas. The boundaries for the Atlanta metropolitan area, forexample, were expanded to include an additional 13 suburban counties. The countiesadded to metropolitan areas in the 1984 redesign have been deleted in this study to makethe housing markets examined here comparable to those in the pre-1984 surveys.

The constant-quality house price indices are used to measure housing inflation rates.Three indices for measuring inflation are computed: a Laspeyres index, which measuresthe price change that occurred in the beginning period’s bundle of housing characteris-tics; a Paasche index, which measures the price change that occurred in the ending

1 A specified owner-occupied unit is a single-family dwelling on less than 10 acres with no commercial, medical,or dental offices on the property.

2 The specified renter-occupied category excludes single-family dwellings on 10 acres or more.


period’s bundle of housing characteristics; and a Fisher index, which is the geometricmean of the Laspeyres and Paasche indices. Finally, a house quality index assesses thegradual improvements taking place in the nation’s housing stock.

The Hedonic Specification

The parameters of the hedonic equations are estimated using a semilogarithmic func-tional form. This specification regresses the log of the dependent variable (rent or housevalue) on a linear combination of housing characteristics and is selected for a variety ofeconomic and statistical reasons. From an economic viewpoint, the semilog functionalform permits the dollar value of a particular characteristic to vary with other character-istics in the bundle. From a statistical viewpoint, preliminary regression results pro-duced residuals that exhibited less heteroskedasticity than the residuals from a linearspecification. The semilogarithmic function form was also used by Gillingham (1975),Palmquist (1979), and others.

The semilog hedonic specification is given by

PX= +e � �, (1)

where P is a vector of house prices (contract rents for renter-occupied dwellings,estimates of house value for owner-occupied units), X is a matrix of housing character-istics described below, � is a vector of unknown hedonic coefficients, and � is the residual.Ordinary least squares is used to estimate the parameters of the transformed equation

Z P X= = +ln ,� � (2)

where � ~ N(0, �2I) so that Z ~ N(X�, �2I), where �2 is the residual variance and I is theidentity matrix. Ordinary least squares yields estimated coefficients

b X X X Z= −( ) ,1T T (3)

where ~b X XN T�, .σ 2 1( )( )−

With this specification, P is log-normal with conditional expectation

E eX

P X{ } = +( )β σ2 2 (4)

and conditional median

M ePX X{ } = � (5)

(Johnson and Kotz 1970).


The dependent variable for the renter equation is the log of the tenant-reported contractrent. The dependent variable for the owner-occupied equation is usually the log of theowner’s estimate of the market value of the property.3 If the property sold within theprevious year, the dependent variable is the log of the transaction price.

Several housing characteristics are included in X:

1. Structural characteristics of the dwelling (dummy variables for the number ofbathrooms, bedrooms, and other rooms; number of units in the structure; dwellingage, age squared, age cubed; and, for owner-occupied dwellings, dummy variablesfor the presence of a garage or a basement)

2. Dwelling equipment (type of heating and air conditioning)

3. Dwelling quality (presence of structural defects, frequency of equipment break-downs, etc.)

4. Neighborhood quality (resident’s opinion of the neighborhood, whether the residentrecently observed rats in the building, etc.)

5. Race of the household head4

6. Contract conditions that may influence house prices (number of persons per room,5

occupant’s length of tenure,6 length of tenure squared, and, for renters, whetherpayments for various utilities are included in the contract rent). Because MSA AHSinformation is collected throughout the survey year, the hedonic specification alsoincludes dummy variables for the month of interview. All variables used in thehedonic equations are defined in table 1. Finally, for most MSAs, the AHS identifiesproperties located in various counties included in the metropolitan area. Thehedonic specification includes county dummy variables whenever the AHS identi-fies county locations. The areas surveyed in the 1974–92 metropolitan AHS, thesurvey years, the counties added in the 1984 redesign, and the location variablesused in the hedonic equations are summarized in table 2.

3 Recently, Goodman and Ittner (1992) reported that homeowners systematically overestimate the value oftheir homes. Robins and West (1977) and Ihlanfeldt and Martinez-Vazquez (1986) had similar findings, butother analysts have had conflicting findings. Follain and Malpezzi (1981) reported that homeowners whoowned their dwellings for long periods tended to underestimate market value. Similarly, Wolters and Woltman(1974) concluded that homeowners underestimated the market value of their properties by 3 percent in the1970 census. Finally, Kish and Lansing (1954) and Kain and Quigley (1972) compared homeowners’ estimateswith professional appraisers’ estimates and concluded that homeowners had unbiased estimates.

4 The race of the head of household serves as a proxy for neighborhood conditions. It would certainly bepreferable to have more direct information on the percentage of the neighborhood that is minority or data onthe dwelling’s census tract, but this information is not available in the AHS.

5 A density variable is included because household density influences the rate of economic depreciation—thegreater the utilization rate, the higher the depreciation rate. Landlords compensate by charging higher rents.

6 Length-of-tenure variables are included in the renter equation to capture discounts available to long-termresidents. They are included in the owner equation to accommodate the potential bias associated with theowner’s estimate of house value (Follain and Malpezzi 1981).


Table 1. Hedonic Equation Variable Definitions

Variable Tenure Definition

I. Dependent variables

LNRENT Renters Log of monthly contract rent

LNVALUE Owners Log of reported selling price if property sold within last12 months; otherwise, log of the owner’s house valueestimate

II. Structural variables

Bathrooms

BATHS10 Both One full bathroom (a room with a flush toilet,(omitted) bathtub or shower, and sink)

BATHS15 Both One-and-a-half bathrooms (a half bath is a room witheither a flush toilet or a bathtub or shower but not thefacilities of a full bath)

BATHS20 Owners Two full baths or two-and-a-half baths

BATHS2P Renters Two or more full baths

BATHS3P Owners Three or more full baths

Bedrooms

BDRMS0 Renters No bedrooms

BDRMS1 Both One bedroom for renter equation; either no bedrooms orone bedroom for owner equation

BDRMS2 Both Two bedrooms(omitted)

BDRMS3 Both Three bedrooms

BDRMS4 Owners Four bedrooms

BDRMS4P Renters 0.25 times the number of bedrooms for units with four ormore bedrooms

BDRMS5P Owners 0.20 times the number of bedrooms for units with five ormore bedrooms

Other rooms

OROOMS1 Renters One other room

OROOMS2 Both Two other rooms(omitted)

OROOMS3 Both Three other rooms

OROOMS4 Owners Four other rooms


Table 1. Hedonic Equation Variable Definitions (continued)


OROOMS4P Renters 0.25 times the number of other rooms for units with fouror more other rooms

OROOMS5 Owners Five other rooms

OROOMS6P Owners 0.17 times the number of other rooms for units with sixor more other rooms

Structure type

DETACHED Both Single-family detached(omittedfor owners)

ATTACHED Both Single-family attached or duplex

THREEOR4 Renters 3- or 4-unit multifamily

FIVETO9 Renters 5- to 9-unit multifamily(omitted)

TENTO19 Renters 10- to 19-unit multifamily

TWENTYP Renters 20-unit or larger multifamily

Dwelling age

AGE Both Dwelling age in years

AGE2 Both AGE2

AGE3 Both AGE3

DAGE Both 1 if structure built before 1940; 0 otherwise

Other structural characteristics

GARAGE Owners Garage or carport

BASEMENT Owners Basement or cellar

Dwelling equipment

Heating

HSYS1 Both Central electric heat

HSYS2 Both Built-in electric units

HSYS3 Both Central gas heat(omitted)

HSYS4 Both Room gas heat




HSYS5 Both Central oil heat

HSYS6 Both Other heating system not specified above (includes unitsthat have no heating equipment as well as dwellingsheated with solar heat, coal, or firewood)

Air conditioning

ACSYS1 Both No air conditioning

ACSYS2 Both At least one room air conditioner but no central airconditioning

ACSYS3 Both Central air conditioning(omitted)

Dwelling quality

BLDGPROB Both Building problems (1 if the unit has two or more of thefollowing problems: basement leaks, roof leaks, opencracks or holes in walls or ceilings, holes in floor, orbroken plaster or peeling paint over an area exceedingone square foot; 0 otherwise)

HALLPROB Renters Public hallway problems (1 if unit is in a multifamilybuilding and has at least two of the following problems:absence of light fixtures in public halls, hazardous stepson common stairs, or stair railings not firmly attached;0 otherwise)

LACKFEAT Both Lack of important features (1 if unit has any of thefollowing deficiencies: lacks complete plumbing; lackscomplete kitchen facilities; sewer system is a chemicaltoilet, privy, outhouse, facilities in another structure, orsome other sewage/toilet facilities; wiring in house notconcealed; or some rooms lack working electrical outlets;0 otherwise)

BREAKDWN Both Multiple equipment breakdowns (1 if unit had any of thefollowing equipment breakdowns: two or more waterbreakdowns lasting six hours or more, two or more flushtoilet breakdowns lasting six hours or more, two or morepublic sewer breakdowns lasting six hours or more, orfuses or circuit breakers blew two or more times withinthe last 90 days; 0 otherwise)

Neighborhood variables

Respondent’s overall opinion of neighborhood

EXCLNBHD Both The respondent is asked to rate the overall quality of theneighborhood between 1 (poor) and 10 (excellent). Thevariable equals 1 if the respondent ranks the neighbor-hood a 9 or a 10 and equals 0 otherwise.




GOODNBHD Both The variable equals 1 if the respondent ranks theneighborhood between 4 and 8 and equals 0 otherwise.

FAIRPOOR Both The variable equals 1 if the respondent ranks the(omitted) neighborhood below 4 and equals 0 otherwise.

Other neighborhood variables

SEERATS Both The variable equals 1 if the respondent observed signs ofrats or mice in the building during the last 90 days andequals 0 otherwise.

ABANDON Both The variable equals 1 if the census enumerator observedabandoned buildings on the street and equals 0otherwise.

LITTER Both The variable equals 1 if respondents are so disturbed bytrash, litter, or junk in the streets (roads), on empty lots,or on properties in the neighborhood that they want tomove and equals 0 otherwise.

CRIME Both Street crime is a problem. The variable equals 1 ifrespondents report that street or neighborhood crime isso disturbing that they want to move and equals0 otherwise.

NOISE Both Street noise is a problem. The variable equals 1 ifrespondents report that street noise is so bad that theywant to move and equals 0 otherwise.

BLACK Both The variable equals 1 if the household head is black andequals 0 otherwise.

HISPAN Both The variable equals 1 if the household head is Hispanicand equals 0 otherwise.

Contract conditions

CROWDS Both Number of persons per room

LOT Both Resident’s length of tenure (difference between the dateof the interview and the date the head of the householdmoved in). Interval midpoints are used for dates re-ported as intervals. The year 1940 is assigned to theopen-ended category (household head moved in before1949).

LOT2 Both LOT2

DLOT Owners The variable equals 1 if the head of household movedinto the dwelling before 1949 and equals 0 otherwise.

EHEATINC Renters The variable equals 1 if payment for electric heat isincluded in contract rent and equals 0 otherwise.




OELECINC Renters The variable equals 1 if payment for electricity isincluded in contract rent but electricity is not used toprovide space heat and equals 0 otherwise.

GHEATINC Renters The variable equals 1 if payment for gas heat is includedin contract rent and equals 0 otherwise.

OILINC Renters The variable equals 1 if payment for oil heat is includedin contract rent and equals 0 otherwise.

OTHERINC Renters The variable equals 1 if payment for other utilities (wateror gas when gas is not used to provide space heat) areincluded in contract rent and equals 0 otherwise.

Month of interview

MONTHj Both The variable equals 1 if the dwelling was surveyed inj = 2, . . . , 9 January or February (for j = 2) and 0 otherwise; March

for j = 3, etc. The survey is conducted from Januarythrough September. For these hedonic equations, Janu-ary and February were combined into one period. Onecategory must be omitted to avoid perfect multico-linearity. The omitted interview month is June.

House Price Indices from the 1984–1992 MSA American Housing Surveys 449T

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part

)T

olla

nd

Co.

(pa

rt)

New

Bri

tain

Cit

yB

rist

ol C

ity

Bal

ance

of

Har

tfor

d C

o.

Hon

olu

lu,

HI

(SM

SA

)N

ot s

urv

eyed

Hou

ston

, T

X (

PM

SA

)19

87,

1991

Wal

ler

Co.

For

t B

end

Co.

Mon

tgom

ery

Co.

Hou

ston

Cit

y (i

nte

r cep

t)B

alan

c e o

f H

arr i

s C

o.B

r azo

r ia

Co.

Indi

anap

olis

, IN

(M

SA

)19

84,

1988

, 19

92N

one

Boo

ne

Co.

Han

cock

Co.

Hen

dric

ks

Co.

Mor

gan

Co.

Sh

elby

Co.

Indi

anap

olis

Cit

y (i

nte

r cep

t)B

alan

ce o

f M

ario

n C

o.H

amil

ton

Cit

yJo

hn

son

Co.

Kan

sas

Cit

y, M

O-K

S (

CM

SA

)19

86,

1990

Ray

Co.

, M

OC

ass

Co.

, M

OL

eave

nw

orth

Co.

, K

SK

ansa

s C

ity

in C

lay

Co.

, M

OL

afay

ette

Co.

, M

OK

ansa

s C

ity

in P

latt

e C

o.,

MO

Mia

mi

Co.

, K

SJo

hn

son

Co.

, K

SK

ansa

s C

ity

in W

yan

dott

e C

o.,

KS


able

2.

Met

rop

oli

tan

AH

S G

eog

rap

hy

, 19

74–1

992

(con

tin

ued

)

Met

ropo

lita

n A

rea

Pos

t-19

83 S

urv

eys

Add

ed C

oun

ties

/Cit

ies

Hed

onic

Loc

atio

n V

aria

bles

Kan

sas

Cit

y in

Jac

kso

n C

o.,

MO

(in

terc

ept)

Bal

ance

of

Wya

ndo

tte

Co.

, K

SB

alan

ce o

f Ja

ckso

n C

o.,

MO

Bal

ance

of

Cla

y C

o.,

MO

Bal

ance

of

Pla

tte

Co.

, M

O

Las

Veg

as,

NV

(S

MS

A)

Not

su

rvey

ed

Los

An

gele

s–L

ong

Bea

ch,

CA

(P

MS

A)

1985

, 19

89N

one

Los

An

gele

s C

ity

(in

terc

ept)

Lon

g B

eac h

Cit

yB

alan

c e o

f L

os A

nge

les

Co.

Lou

isvi

lle,

KY

-IN

(S

MS

A)

Not

su

r vey

ed

Mad

ison

, W

I (S

MS

A)

Not

su

r vey

ed

Mem

phis

, T

N-A

R-M

S (

MS

A)

1984

, 19

88,

1992

Tip

ton

Co.

, T

NC

r itt

ende

n C

o.,

AR

DeS

oto

Co.

, M

SM

emph

is C

ity,

TN

(in

ter c

ept)

Bal

ance

of

Sh

elby

Co.

, T

N

Mia

mi–

For

t L

aude

rdal

e, F

L (

CM

SA

)19

86,

1990

Bro

war

d C

o.M

iam

i C

ity

(in

ter c

ept)

Bal

ance

of

Dad

e C

o.

Mil

wau

kee

, W

I (P

MS

A)

1984

, 19

88N

one

Oza

uk

ee C

o.W

ash

ingt

on C

o.M

ilw

auk

ee C

ity

(in

ter c

ept)

Wau

kes

ha

Co.

Bal

ance

of

Mil

wau

kee

Co.

Min

nea

poli

s–S

t. P

aul,

MN

-WI

(MS

A)

1985

, 19

89Is

anti

Co.

, M

NA

nok

a C

o.,

MN

Ch

isag

o C

o.,

MN

Dak

ota

Co.

, M

NW

r igh

t C

o.,

MN

Was

hin

gton

Co.

, M

NS

t. C

r oix

Co.

, W

IM

inn

eapo

lis

Cit

y, M

N (

inte

r cep

t)


able

2.

Met

rop

oli

tan

AH

S G

eog

rap

hy

, 19

74–1

992

(con

tin

ued

)

Met

ropo

lita

n A

rea

Pos

t-19

83 S

urv

eys

Add

ed C

oun

ties

/Cit

ies

Hed

onic

Loc

atio

n V

aria

bles

Car

ver

Co.

, M

NS

t. P

aul

Cit

y, M

NS

cott

Co.

, M

NB

alan

ce o

f H

enn

epin

Co.

, M

NB

alan

ce o

f R

amse

y C

o.,

MN

New

Orl

ean

s, L

A (

MS

A)

1986

, 19

90S

t. J

ohn

th

e B

apti

st P

aris

hS

t. B

ern

ard

Par

ish

St.

Ch

arle

s P

aris

hN

ew O

rlea

ns

Cit

y (i

nte

rcep

t)Je

ffer

son

Par

ish

St.

Tam

man

y P

aris

h

New

Yor

k–N

assa

u–S

uff

olk

, N

Y19

87,

1991

Ora

nge

Co.

Wes

tch

este

r C

o.(P

MS

A)

Pu

tnam

Co.

New

Yor

k C

ity

in B

ron

x C

o.N

ew Y

ork

Cit

y in

Kin

gs C

o. (

inte

rcep

t)N

ew Y

ork

Cit

y in

Qu

een

s C

o.N

ew Y

ork

Cit

y in

Ric

hm

ond

Co.

Nas

sau

Co.

Su

ffol

k C

o.

New

ark

–Nor

thea

ster

n19

87,

1991

Par

t of

nor

ther

n N

ew J

erse

yN

ewar

k C

ity

(in

ter c

ept)

New

Jer

sey,

NJ

(PM

SA

)H

uds

on C

o.M

orr i

s C

o.Je

rsey

Cit

yB

alan

ce o

f E

ssex

Co.

Hu

nte

rdon

Co.

Som

erse

t C

o.M

iddl

esex

Co.

Mon

mou

th C

o.O

cean

Co.

New

por t

New

s–H

ampt

on,

VA

1984

, 19

88,

1992

Par

t of

Nor

folk

–Vir

gin

iaN

ewpo

r t N

ews

Cit

y (i

nte

r cep

t)(S

MS

A)

Bea

ch–N

ewpo

r t N

ews

MS

AH

ampt

on C

ity

Bal

ance

of

Yor

k C

o.


able

2.

Met

rop

oli

tan

AH

S G

eog

rap

hy

, 19

74–1

992

(con

tin

ued

)

Met

ropo

lita

n A

rea

Pos

t-19

83 S

urv

eys

Add

ed C

oun

ties

/Cit

ies

Hed

onic

Loc

atio

n V

aria

bles

Nor

folk

–Vir

gin

ia B

each

–19

84,

1988

, 19

92Ja

mes

Cit

y C

o.N

orfo

lk C

ity

(in

terc

ept)

New

port

New

s, V

A (

MS

A)

Glo

uce

ster

Co.

Vir

gin

ia B

each

Cit

yW

illi

amsb

urg

Cit

yN

ewpo

rt N

ews

Cit

yS

uff

olk

Cit

yS

uff

olk

Cit

yC

hes

apea

ke

Cit

yP

orts

mou

th C

ity

Por

tsm

outh

Cit

yH

ampt

on C

ity

Nor

folk

Cit

yC

hes

apea

ke

Cit

yV

irgi

nia

Bea

ch C

ity

Yor

k C

o.

Ok

lah

oma

Cit

y, O

K (

MS

A)

1984

, 19

88,

1992

Log

an C

o.C

anad

ian

Co.

McC

lain

Co.

Cle

vela

nd

Co.

Pot

taw

atom

ie C

o.O

kla

hom

a C

o. (

inte

r cep

t)

Om

aha,

NE

-IA

(S

MS

A)

Not

su

r vey

ed

Or l

ando

, F

L (

SM

SA

)N

ot s

ur v

eyed

Pat

erso

n-C

lift

on-P

assa

ic,

NJ

(PM

SA

)19

87,

1991

Par

t of

nor

ther

n N

ew J

erse

yP

assa

ic C

o. (

inte

r cep

t)S

uss

ex C

o.B

erge

n C

o.

Ph

ilad

elph

ia,

PA

-NJ

(PM

SA

)19

85,

1989

Non

eB

uck

s C

o.,

PA

Ch

este

r C

o.,

PA

Bu

r lin

gton

Co.

, N

JC

amde

n C

o.,

NJ

Glo

uce

ster

Co.

, N

JP

hil

adel

phia

Cit

y, P

A (

inte

r cep

t)M

ontg

omer

y C

o.,

PA

Del

awar

e C

o.,

PA

Ph

oen

ix,

AZ

(M

SA

)19

85,

1989

Non

eP

hoe

nix

Cit

y (i

nte

r cep

t)M

esa

Cit

yB

alan

ce o

f M

aric

opa

Co.


able

2.

Met

rop

oli

tan

AH

S G

eog

rap

hy

, 19

74–1

992

(con

tin

ued

)

Met

ropo

lita

n A

rea

Pos

t-19

83 S

urv

eys

Add

ed C

oun

ties

/Cit

ies

Hed

onic

Loc

atio

n V

aria

bles

Pit

tsbu

rgh

, P

A (

CM

SA

)19

86,

1990

Fay

ette

Co.

Bea

ver

Co.

Was

hin

gton

Co.

Pit

tsbu

rgh

Cit

y (i

nte

rcep

t)B

alan

ce o

f A

lleg

hen

y C

o.W

estm

orel

and

Co.

Por

tlan

d, O

R-W

A (

CM

SA

)19

86,

1990

Yam

hil

l C

o.,

OR

Cla

ckam

as C

o.,

OR

Cla

rk C

o.,

WA

Por

tlan

d C

ity,

OR

(in

terc

ept)

Bal

ance

of

Mu

ltn

omah

Co.

, O

RW

ash

ingt

on C

o.,

OR

Pr o

vide

nc e

-Paw

tuc k

et-W

arw

ick

,19

84,

1988

, 19

92N

ewpo

r t C

o.,

RI

Br i

stol

Co.

, R

IR

I-M

A (

PM

SA

)K

ent

Co.

, R

IP

r ovi

den

c e C

o.,

RI

Was

hin

gton

Co.

, R

IN

orfo

lk C

o.,

MA

Pro

vide

nce

Cit

y, R

I (i

nte

r cep

t)W

arw

ick

Cit

y, R

IC

r an

ston

Cit

y, R

I

Ral

eigh

, N

C (

SM

SA

)N

ot s

urv

eyed

Roc

hes

ter ,

NY

(M

SA

)19

86,

1990

On

tar i

o C

o.L

ivin

gsto

n C

o.O

r lea

ns

Co.

Way

ne

Co.

Roc

hes

ter

Cit

y (i

nte

r cep

t)B

alan

ce o

f M

onro

e C

o.

Sac

r am

ento

, C

A (

SM

SA

)N

ot s

urv

eyed

Sag

inaw

, M

I (S

MS

A)

Not

su

rvey

ed


able

2.

Met

rop

oli

tan

AH

S G

eog

rap

hy

, 19

74–1

992

(con

tin

ued

)

Met

ropo

lita

n A

rea

Pos

t-19

83 S

urv

eys

Add

ed C

oun

ties

/Cit

ies

Hed

onic

Loc

atio

n V

aria

bles

St.

Lou

is,

MO

-IL

(C

MS

A)

1987

, 19

91Je

rsey

Co.

, IL

Jeff

erso

n C

o.,

MO

Mon

roe

Co.

, IL

St.

Ch

arle

s C

o.,

MO

Cli

nto

n C

o.,

ILM

adis

on C

o.,

ILS

t. L

ouis

Cit

y, M

O (

inte

rcep

t)B

alan

ce o

f S

t. L

ouis

Co.

, M

OS

t. C

lair

Co.

, IL

Sal

t L

ake

Cit

y, U

T (

MS

A)

1984

, 19

88,

1992

Web

er C

o.S

alt

Lak

e C

ity

(in

terc

ept)

Bal

ance

of

Sal

t L

ake

Co.

Dav

is C

o.

San

An

ton

io,

TX

(M

SA

)19

86,

1990

Com

al C

o.S

an A

nto

nio

Cit

y (i

nte

r cep

t)B

alan

c e o

f B

exar

Co.

Gu

adal

upe

Co.

San

Ber

nar

din

o–R

iver

side

–19

86,

1990

Non

eS

an B

ern

ardi

no

Cit

yO

nta

r io,

CA

(P

MS

A)

Riv

ersi

de C

ity

(in

ter c

ept)

Bal

ance

of

Riv

ersi

de C

o.B

alan

ce o

f S

an B

ern

ardi

no

Co.

San

Die

go,

CA

(M

SA

)19

87,

1991

Non

eS

an D

iego

Cit

y (i

nte

r cep

t)B

alan

ce o

f S

an D

iego

Co.

San

Fra

nc i

sco–

Oak

lan

d, C

A19

85,

1989

Non

eS

an M

ateo

Co.

(PM

SA

)C

ontr

a C

osta

Co.

Mar

in C

o.S

an F

ran

c isc

o C

ity

(in

ter c

ept)

Oak

lan

d C

ity

Bal

ance

of

Ala

med

a C

o.

San

Jos

e, C

A (

adde

d in

198

4) (

MS

A)

1984

, 19

88S

an J

ose

Cit

y (i

nte

r cep

t)S

un

nyv

ale

Cit

yB

alan

ce o

f S

anta

Cla

ra C

o.


able

2.

Met

rop

oli

tan

AH

S G

eog

rap

hy

, 19

74–1

992

(con

tin

ued

)

Met

ropo

lita

n A

rea

Pos

t-19

83 S

urv

eys

Add

ed C

oun

ties

/Cit

ies

Hed

onic

Loc

atio

n V

aria

bles

Sea

ttle

-Eve

rett

, W

A (

CM

SA

)19

87,

1991

Par

t of

Sea

ttle

-S

noh

omis

h C

o.T

acom

a M

SA

Sea

ttle

Cit

y (i

nte

rcep

t)B

alan

ce o

f K

ing

Co.

Spo

kan

e, W

A (

SM

SA

)N

ot s

urv

eyed

Spr

ingf

ield

-Ch

icop

ee-H

olyo

ke,

MA

(S

MS

A)

Not

su

rvey

ed

Tac

oma,

WA

(M

SA

)19

87,

1991

Par

t of

Sea

ttle

-P

ierc

e C

o. (

inte

rcep

t)T

acom

a M

SA

Tam

pa–S

t. P

eter

sbu

rg,

FL

(ad

ded

in 1

985)

(M

SA

)19

85,

1989

Her

nan

do C

o.P

asco

Co.

Tam

pa C

ity

(in

ter c

ept)

St.

Pet

ersb

ur g

Cit

yB

alan

c e o

f P

inel

las

Co.

Bal

anc e

of

Hil

lsbo

r ou

gh C

o.

Was

hin

gton

, D

C-M

D-V

A (

MS

A)

1985

, 19

89F

rede

r ic k

Co.

, M

DM

ontg

omer

y C

o.,

MD

Sta

ffor

d C

o.,

VA

Ar l

ingt

on C

o.,

VA

Ch

arle

s C

o.,

MD

Lou

dou

n C

o.,

VA

Cal

ver t

Co.

, M

DW

ash

ingt

on,

DC

(in

ter c

ept)

Pr i

nc e

Geo

rge’

s C

o.,

MD

Fai

r fax

Co.

, V

A

Wic

hit

a, K

S (

SM

SA

)N

ot s

urv

eyed

Not

e : M

SA

= m

etr o

poli

tan

sta

tist

ical

ar e

a; C

MS

A =

con

soli

date

d m

etr o

poli

tan

sta

tist

ical

ar e

a; P

MS

A =

pr i

mar

y m

etr o

poli

tan

sta

tist

ical

ar e

a; S

MS

A =

stan

dard

met

r opo

lita

n s

tati

stic

al a

r ea.


MSA house price indices are computed by pricing a constant bundle of housing charac-teristics using MSA estimated hedonic coefficients. Gillingham (1975); Goodman (1978);Follain and Malpezzi (1980); Linneman (1980); Malpezzi, Ozanne, and Thibodeau (1980);Thibodeau (1989, 1992); and others have used this procedure to measure the price ofhousing. Predicted values of the dependent variable in the transformed linear model areobtained by substituting estimated coefficients b and average housing characteristics X0

into equation (2) to yield

z

z NT T

0 0

0 02

01

0

=

( )( )−

X b

X X X X X

,

~ ,where � σ(6)

The constant-quality bundle that is priced is the average bundle of housing characteris-tics for all residential dwellings in large MSAs (census places with populations exceeding100,000). Average characteristics for the stock of urban housing are obtained from the1985 National AHS. The National AHS surveys about 70,000 residential dwellings inodd-numbered years.

House price indices are computed for the entire stock of housing within the metropolitanarea and for three quality-segmented submarkets: new housing, existing standard-quality housing, and substandard housing. For the entire-metropolitan-area house priceindex, average (tenure-specific) characteristics are computed for all residential dwellingslocated in large MSAs. Hence the housing characteristics that are priced represent theentire stock of housing in large urban areas rather than the characteristics peculiar toany one urban area. Average dwelling characteristics for substandard housing arecomputed using a definition of substandard housing formerly employed by HUD (1978).According to that definition, a residential dwelling is substandard if one or more of thefollowing conditions hold:

Plumbing. Unit lacks or shares complete plumbing (hot and cold running water,flush toilet, and bathtub or shower inside the structure).Kitchen. Unit lacks or shares a complete kitchen (installed sink with piped water,range or cook stove, and mechanical refrigerator).Sewage. Absence of a public sewer, septic tank, cesspool, or chemical toilet.Heating. There are no means of heating; or unit is heated by unvented room heatersburning gas, oil, or kerosene; or unit is heated by fireplace, stove, or portable roomheater (does not apply in South Census region).Maintenance. Unit suffers from any two of the following defects: leaking roof, opencracks or holes in interior walls or ceilings, holes in the interior floor, broken plasteror peeling paint (over one square foot) on interior walls or ceilings.Public hall. Unit suffers from any two of the following defects: public halls lacklight fixtures; loose, broken, or missing steps on common stairways; stair railingsloose or missing.Toilet access. Access to sole flush toilet is through one of two or more bedroomsused for sleeping (applies only to households with children under 18 years old).Electrical. Unit has exposed wiring, fuses blew or circuit breakers tripped three ormore times in the last 90 days, and unit lacks working outlet in one or more rooms.


Average dwelling characteristics for new housing are computed for dwellings located inlarge MSAs that are less than three years old and not substandard according to thisdefinition. Existing standard-quality housing is defined to be housing that is neither newnor substandard.

The semilog hedonic specification introduces a statistical problem for computing houseprice indices. That is, the objective is to compute an unbiased estimator for either E{p0}or M{p0} rather than an unbiased estimator for the log of the house price. One potentialestimator, the “naive transformation,” takes the exponential of the predicted valueobtained from the semilog equation

p e ez0

0= = X b0 . (7)

Numerous authors (Aitchison and Brown 1957; Bradu and Mundlak 1970; Dhrymes1962; Finney 1941; Goldberger 1968; Haworth and Vincent 1979; Heien 1968; Meulenberg1965; Stynes, Peterson, and Rosenthal 1986; Teekens and Koerts 1972) have examinedthe statistical properties of the naive transformation and concluded that it is (1)asymptotically unbiased for the median of the house price distribution M{p0}, (2) biasedfor M{p0} in finite samples, and (3) asymptotically biased for the mean of the pricedistribution E{p0}. The bias introduced with the naive transformation can be substantial.Stynes, Peterson, and Rosenthal (1986) have demonstrated that the finite-sample bias inpublished studies on travel demand can be as high as 26 percent.

The finite sample unbiased estimator for median price in a semilog regression given byGoldberger (1968) is

p e F s T KM0

2 10 1

12

* ; , ,= − − − ( )

−X b X X X X0T T

0 (8)

where

F w v cf cw

jj

j

j

; ,!

,( ) =( )

=

∞

∑0

f

v vv jj

j

= ( ) ( )+( )

2 22�

�,

v T K= − ,

Γ t y e dy tt y( ) = >− −∞

∫ 1

00for ,

s

T K2 1

1=

− −ˆ ˆ ,u uT

T = the sample size,

K = the number of estimated parameters, and

û = the estimated residual.


Finite-sample unbiased estimators for median house prices are computed using anapproximation to equation (8) (for details, see Thibodeau 1992). Correcting for the finite-sample bias is important even with the relatively large samples used here because thebias is a function of the residual variance, and the residual variance for the 1974–83hedonic equations increased systematically with the survey year.

Hedonic House Price Indices

MSA House Prices

MSA house price indices are listed in table 3 (for rental housing) and table 4 (for owner-occupied housing). The prices are listed alphabetically by metropolitan area beginningwith the places surveyed in 1984. Each table has six columns. The first two columns listthe MSA followed by the survey year. The last four columns list the house price indices.In table 3, PRMSA is the price of shelter rental housing services for the entire metropoli-tan area, PRNEW is the price of new rental housing services, PREXT is the price ofexisting standard-quality rental housing services, and PRSUB is the price of substan-dard rental housing services. The owner-occupied house price indices in table 4 aredefined similarly.

A year-by-year comparison of house prices indicates that New York City and Californiametropolitan areas consistently had the highest shelter rents. Shelter rents in thesehigh-priced housing markets were 1.9 to 3.2 times those in the least expensive housingmarkets. With some exceptions, the South (Birmingham, Houston, Oklahoma City, SanAntonio, and Tampa) had the markets with the lowest shelter rent. California MSAs(Anaheim, San Francisco, and San Jose) also had the highest priced owner-occupiedhousing. California MSA house prices were 2.9 to 4.6 times those in the least expensiveMSAs and 1.9 to 3.6 times the metropolitan average. Inexpensive owner-occupiedhousing was located throughout the South and the Midwest (Detroit, Houston, KansasCity, Memphis, Oklahoma City, St. Louis, and Tampa).

Measuring House Price Inflation

House price inflation is measured with three indices: a Laspeyres index, a Paasche index,and a Fisher index. The Laspeyres and Paasche indices are ratios of the ending-periodhousing expenditure to the beginning-period expenditure with expected expenditurescomputed using the bundle of housing characteristics from the beginning (Laspeyres) orending (Paasche) period. The Fisher index is simply the square root of the product of theLaspeyres and Paasche indices.

Both the Laspeyres and the Paasche indices price a constant bundle of housing charac-teristics over time. However, housing consumers modify their consumption patterns overtime in response to changes in price and in housing characteristics. An index is said tobe superlative if it accommodates changes in consumers’ expenditure patterns (Diewert1976). The Fisher index is superlative; the Laspeyres and Paasche indices are not.


able

3.

Mo

nth

ly P

rice

s o

f R

enta

l H

ou

sin

g S

erv

ices

PR

MS

AP

RN

EW

PR

EX

TP

RS

UB

MS

AS

urv

ey Y

ear

($)

($)

($)

($)

Bir

min

gham

, A

L19

8416

4.25

262.

4717

3.50

122.

83B

uff

alo,

NY

1984

208.

6328

3.88

217.

8016

7.16

Cle

vela

nd,

OH

1984

235.

5235

8.70

246.

5917

8.36

Indi

anap

olis

, IN

1984

186.

0731

4.69

195.

9713

8.85

Mem

phis

, T

N-A

R-M

S19

8417

9.99

305.

6019

1.06

130.

68M

ilw

auk

ee,

WI

1984

278.

5742

4.67

292.

8721

2.71

New

port

New

s–H

ampt

on,

VA

1984

225.

0331

7.69

233.

6218

3.19

Nor

folk

–Vir

gin

ia B

each

–New

port

New

s, V

A19

8423

6.90

337.

5124

9.06

176.

80O

kla

hom

a C

ity,

OK

1984

214.

6132

7.21

224.

4916

8.12

Pro

vide

nce

-Paw

tuck

et-W

arw

ick

, R

I-M

A19

8424

5.12

414.

6025

5.96

191.

34S

alt

Lak

e C

ity,

UT

1984

254.

8638

4.26

259.

8822

2.91

San

Jos

e, C

A19

8444

2.88

648.

1946

4.08

341.

32

Bos

ton

, M

A-N

H19

8536

3.01

456.

7537

8.67

292.

88D

alla

s, T

X19

8531

0.23

416.

3332

3.75

254.

77D

etr o

it,

MI

1985

281.

7545

3.30

293.

6921

6.65

For

t W

orth

, T

X19

8523

8.42

354.

5524

4.56

201.

91L

os A

nge

les–

Lon

g B

each

, C

A19

8541

4.95

580.

6343

1.73

336.

96M

inn

eapo

lis–

St.

Pau

l, M

N-W

I19

8532

5.87

435.

9933

4.76

276.

75P

hil

adel

phia

, P

A-N

J19

8530

0.22

450.

7231

4.45

234.

06P

hoe

nix

, A

Z19

8526

4.62

392.

7827

6.15

210.

56S

an F

ran

c isc

o–O

akla

nd,

CA

1985

431.

9062

5.82

447.

3335

4.57

Tam

pa–S

t. P

eter

sbu

rg,

FL

1985

210.

4931

1.97

220.

1616

5.29

Was

hin

gton

, D

C-M

D-V

A19

8537

3.63

533.

9938

3.36

314.

45

An

ahei

m–S

anta

An

a, C

A19

8650

9.22

672.

9751

4.23

470.

07C

inc i

nn

ati,

OH

-KY

-IN

1986

248.

1935

7.28

262.

3718

6.13

Den

ver ,

CO

1986

335.

0243

4.61

347.

2927

9.96

Kan

sas

Cit

y, M

O-K

S19

8622

6.01

386.

1323

8.26

169.

15M

iam

i–F

ort

Lau

derd

ale,

FL

1986

322.

1941

3.22

332.

2127

2.41

New

Or l

ean

s, L

A19

8625

3.75

311.

7926

2.55

218.

05P

itts

burg

h,

PA

1986

205.

7831

8.23

217.

5715

4.31

Por

tlan

d, O

R-W

A19

8628

7.69

439.

9529

9.94

227.

72R

och

este

r , N

Y19

8628

5.91

364.

0730

3.49

215.

83S

an A

nto

nio

, T

X19

8620

9.03

322.

1621

6.48

171.

84S

an B

ern

ardi

no–

Riv

ersi

de–O

nta

r io,

CA

1986

313.

2648

4.41

324.

0325

6.70

Atl

anta

, G

A19

8729

0.22

419.

6730

5.19

226.

45B

alti

mor

e, M

D19

8729

3.58

415.

3230

4.38

236.

63


able

3.

Mo

nth

ly P

rice

s o

f R

enta

l H

ou

sin

g S

erv

ices

(con

tin

ued

)

PR

MS

AP

RN

EW

PR

EX

TP

RS

UB

MS

AS

urv

ey Y

ear

($)

($)

($)

($)

Ch

icag

o, I

L19

8735

3.39

548.

9836

7.09

284.

46C

olu

mbu

s, O

H19

8724

9.65

402.

5326

0.33

194.

72H

artf

ord,

CT

1987

377.

0558

0.08

390.

1130

2.67

Hou

ston

, T

X19

8725

1.15

331.

8925

8.47

218.

98N

ew Y

ork

–Nas

sau

–Su

ffol

k,

NY

1987

498.

0771

7.52

517.

0840

6.40

New

ark

–Nor

thea

ster

n N

ew J

erse

y, N

J19

8742

0.11

750.

5044

1.92

313.

83P

ater

son

-Cli

fton

-Pas

saic

, N

J19

8745

7.10

656.

9547

9.85

354.

57S

t. L

ouis

, M

O-I

L19

8724

4.00

367.

3125

5.76

188.

15S

an D

iego

, C

A19

8742

7.99

576.

1343

8.46

372.

03S

eatt

le-E

vere

tt,

WA

1987

379.

7049

8.17

391.

6132

0.94

Tac

oma,

WA

1987

297.

9238

7.80

313.

2523

5.58

Bir

min

gham

, A

L19

8818

7.80

264.

5920

2.50

135.

14B

uff

alo,

NY

1988

267.

9837

4.98

277.

1222

1.70

Cle

vela

nd,

OH

1988

306.

7656

0.74

317.

8823

8.09

Indi

anap

olis

, IN

1988

246.

0338

7.41

256.

1319

1.57

Mem

phis

, T

N-A

R-M

S19

8824

3.55

376.

9725

5.27

187.

83M

ilw

auk

ee,

WI

1988

323.

1553

4.53

332.

0226

8.61

New

por t

New

s–H

ampt

on,

VA

1988

294.

1241

4.77

305.

5424

0.01

Nor

folk

–Vir

gin

ia B

each

–New

por t

New

s, V

A19

8829

8.27

413.

4930

7.37

244.

47O

kla

hom

a C

ity,

OK

1988

184.

0228

0.96

192.

6014

3.95

Pro

vide

nce

-Paw

tuck

et-W

arw

ick

, R

I-M

A19

8839

8.45

584.

7341

4.48

321.

17S

alt

Lak

e C

ity,

UT

1988

245.

5533

5.84

252.

8121

0.34

San

Jos

e, C

A19

8859

6.34

753.

1661

5.78

502.

92

Bos

ton

, M

A-N

H19

8955

7.44

701.

6358

8.26

432.

18D

alla

s, T

X19

8926

8.87

424.

0428

0.68

212.

10D

etr o

it,

MI

1989

344.

2854

3.62

353.

3427

4.48

For

t W

orth

, T

X19

8924

9.99

349.

9325

9.46

205.

14L

os A

nge

les–

Lon

g B

each

, C

A19

8954

8.42

691.

9156

9.89

453.

48M

inn

eapo

lis–

St.

Pau

l, M

N-W

I19

8936

4.24

466.

4738

2.13

287.

15P

hil

adel

phia

, P

A-N

J19

8939

0.39

576.

8440

2.34

322.

41P

hoe

nix

, A

Z19

8932

7.97

440.

3133

5.44

286.

21S

an F

ran

c isc

o–O

akla

nd,

CA

1989

572.

8571

2.46

589.

3249

8.24

Tam

pa–S

t. P

eter

sbu

rg,

FL

1989

234.

1037

8.16

237.

1621

6.04

Was

hin

gton

, D

C-M

D-V

A19

8948

4.07

631.

7350

0.56

402.

65


able

3.

Mo

nth

ly P

rice

s o

f R

enta

l H

ou

sin

g S

erv

ices

(co

nti

nu

ed)

PR

MS

AP

RN

EW

PR

EX

TP

RS

UB

MS

AS

urv

ey Y

ear

($)

($)

($)

($)

An

ahei

m–S

anta

An

a, C

A19

9066

4.64

823.

6467

9.68

586.

55C

inci

nn

ati,

OH

-KY

-IN

1990

281.

6137

7.27

290.

3923

7.42

Den

ver,

CO

1990

330.

2845

6.60

337.

3128

7.44

Kan

sas

Cit

y, M

O-K

S19

9027

3.88

410.

7528

8.11

208.

75M

iam

i–F

ort

Lau

derd

ale,

FL

1990

440.

5753

4.72

457.

9836

4.05

New

Orl

ean

s, L

A19

9027

9.78

370.

3728

7.38

244.

29P

itts

burg

h,

PA

1990

276.

6941

4.52

286.

5922

5.57

Por

tlan

d, O

R-W

A19

9035

1.20

541.

9736

6.91

274.

33R

och

este

r, N

Y19

9036

9.78

449.

7538

1.30

316.

05S

an A

nto

nio

, T

X19

9025

8.62

405.

9826

9.83

205.

11S

an B

ern

ardi

no–

Riv

ersi

de–O

nta

rio,

CA

1990

389.

9452

8.88

401.

5432

9.69

Atl

anta

, G

A19

9132

6.39

503.

8234

2.48

252.

71B

alti

mor

e, M

D19

9138

0.82

568.

9339

1.59

314.

34C

hic

ago,

IL

1991

436.

0470

1.39

461.

7532

4.47

Col

um

bus,

OH

1991

293.

4546

4.34

303.

4323

8.29

Har

tfor

d, C

T19

9148

5.07

649.

9750

7.51

379.

80H

oust

on,

TX

1991

284.

4338

3.94

296.

5323

1.46

New

Yor

k–N

assa

u–S

uff

olk

, N

Y19

9164

0.08

977.

4665

5.17

551.

79N

ewar

k–N

orth

east

ern

New

Jer

sey,

NJ

1991

514.

1745

7.29

527.

9446

1.45

Pat

erso

n-C

lift

on-P

assa

ic,

NJ

1991

505.

3782

9.54

524.

0939

9.30

St.

Lou

is,

MO

-IL

1991

297.

1742

9.91

304.

3524

9.93

San

Die

go,

CA

1991

605.

1765

8.36

624.

3352

6.61

Sea

ttle

-Eve

rett

, W

A19

9147

4.51

618.

3949

4.76

385.

88T

acom

a, W

A19

9138

0.08

648.

4039

3.37

306.

59

Bir

min

gham

, A

L19

9221

5.82

350.

6922

6.70

163.

46C

leve

lan

d, O

H19

9232

5.60

472.

1334

2.83

241.

90In

dian

apol

is,

IN19

9230

1.63

447.

7231

2.41

246.

98M

emph

is,

TN

-AR

-MS

1992

249.

4341

6.72

263.

4118

5.49

New

por t

New

s–H

ampt

on,

VA

1992

342.

2446

2.32

358.

7027

0.70

Nor

folk

–Vir

gin

ia B

each

–New

por t

New

s, V

A19

9232

1.74

443.

3033

2.75

264.

04O

kla

hom

a C

ity,

OK

1992

221.

9432

6.18

231.

1117

8.27

Pro

vide

nce

-Paw

tuck

et-W

arw

ick

, R

I-M

A19

9241

6.88

595.

5743

2.06

341.

97S

alt

Lak

e C

ity,

UT

1992

319.

7845

2.79

328.

9027

1.11


able

4.

Pri

ces

of

Ow

ner

-Occ

up

ied

Ho

usi

ng

PO

MS

AP

ON

EW

PO

EX

TP

OS

UB

MS

AS

urv

ey Y

ear

($)

($)

($)

($)

Bir

min

gham

, A

L19

8444

,667

73,4

0146

,066

30,0

53B

uff

alo,

NY

1984

43,4

7984

,629

44,0

7933

,345

Cle

vela

nd,

OH

1984

56,4

7399

,730

57,0

1346

,344

Indi

anap

olis

, IN

1984

44,3

2578

,585

44,7

1336

,421

Mem

phis

, T

N-A

R-M

S19

8443

,345

85,9

8344

,175

31,4

48M

ilw

auk

ee,

WI

1984

60,1

2910

7,34

560

,964

46,2

78N

ewpo

rt N

ews–

Ham

pton

, V

A19

8453

,957

82,0

1454

,588

43,5

95N

orfo

lk–V

irgi

nia

Bea

ch–N

ewpo

rt N

ews,

VA

1984

58,5

2886

,629

59,3

3845

,148

Ok

lah

oma

Cit

y, O

K19

8455

,666

97,0

8856

,553

43,5

30P

rovi

den

ce-P

awtu

cket

-War

wic

k,

RI-

MA

1984

62,1

1111

2,62

662

,328

53,0

81S

alt

Lak

e C

ity,

UT

1984

64,3

9792

,959

64,9

1355

,821

San

Jos

e, C

A19

8414

3,29

016

0,54

714

4,49

313

3,13

4

Bos

ton

, M

A-N

H19

8511

3,61

316

2,32

611

4,58

697

,764

Dal

las,

TX

1985

77,2

9411

9,12

879

,987

52,2

72D

etr o

it,

MI

1985

43,4

6386

,496

44,1

3932

,667

For

t W

orth

, T

X19

8554

,238

103,

878

55,0

2941

,154

Los

An

gele

s–L

ong

Bea

ch,

CA

1985

131,

516

175,

194

133,

452

108,

084

Min

nea

poli

s–S

t. P

aul,

MN

-WI

1985

66,1

1010

6,36

765

,978

60,5

13P

hil

adel

phia

, P

A-N

J19

8557

,246

96,7

7458

,287

43,3

33P

hoe

nix

, A

Z19

8572

,764

119,

925

73,2

4661

,477

San

Fra

nc i

sco–

Oak

lan

d, C

A19

8513

6,89

416

9,91

113

9,46

111

0,13

7T

ampa

–St.

Pet

ersb

urg

, F

L19

8548

,169

86,0

1848

,656

40,1

95W

ash

ingt

on,

DC

-MD

-VA

1985

92,4

5512

2,11

093

,576

78,4

35

An

ahei

m–S

anta

An

a, C

A19

8614

2,50

119

8,28

614

2,86

512

8,99

2C

inc i

nn

ati,

OH

-KY

-IN

1986

52,0

1083

,705

52,5

4942

,613

Den

ver ,

CO

1986

80,8

4313

1,43

080

,908

73,4

62K

ansa

s C

ity,

MO

-KS

1986

45,1

3990

,225

46,0

2131

,807

Mia

mi–

For

t L

aude

rdal

e, F

L19

8681

,140

123,

200

81,2

7174

,790

New

Or l

ean

s, L

A19

8666

,740

98,0

1668

,300

50,0

98P

itts

burg

h,

PA

1986

48,6

6311

2,88

848

,924

38,8

76P

ortl

and,

OR

-WA

1986

64,7

6310

3,56

065

,068

55,6

19R

och

este

r , N

Y19

8667

,393

112,

661

68,4

3851

,353

San

An

ton

io,

TX

1986

61,8

1210

5,45

763

,139

44,9

02S

an B

ern

ardi

no–

Riv

ersi

de–O

nta

r io,

CA

1986

90,2

0812

6,11

691

,442

73,6

35


able

4. P

rice

s o

f O

wn

er-O

ccu

pie

d H

ou

sin

g (c

onti

nu

ed)

PO

MS

AP

ON

EW

PO

EX

TP

OS

UB

MS

AS

urv

ey Y

ear

($)

($)

($)

($)

Atl

anta

, G

A19

8772

,049

104,

106

72,8

4963

,697

Bal

tim

ore,

MD

1987

77,1

8914

1,96

777

,167

65,4

42C

hic

ago,

IL

1987

81,1

7012

5,11

581

,989

67,2

53C

olu

mbu

s, O

H19

8758

,210

89,3

5558

,516

50,3

43H

artf

ord,

CT

1987

135,

427

217,

181

136,

782

111,

657

Hou

ston

, T

X19

8757

,624

79,7

9659

,071

42,8

19N

ew Y

ork

–Nas

sau

–Su

ffol

k,

NY

1987

158,

675

251,

603

162,

473

115,

945

New

ark

–Nor

thea

ster

n N

ew J

erse

y, N

J19

8715

6,58

123

6,38

215

7,38

613

4,02

0P

ater

son

-Cli

fton

-Pas

saic

, N

J19

8717

6,16

429

2,33

117

5,69

416

5,74

2S

t. L

ouis

, M

O-I

L19

8752

,204

95,8

0053

,109

38,6

50S

an D

iego

, C

A19

8713

4,16

318

0,11

513

5,91

211

2,18

9S

eatt

le-E

vere

tt,

WA

1987

82,2

7911

8,88

083

,045

72,2

16T

acom

a, W

A19

8779

,169

130,

624

80,9

9057

,406

Bir

min

gham

, A

L19

8850

,310

86,8

2851

,685

35,6

78B

uff

alo,

NY

1988

53,1

1210

0,14

353

,626

42,3

07C

leve

lan

d, O

H19

8856

,166

102,

478

56,8

8743

,236

Indi

anap

olis

, IN

1988

50,3

4198

,339

51,2

4737

,122

Mem

phis

, T

N-A

R-M

S19

8859

,083

107,

989

60,5

3040

,708

Mil

wau

kee

, W

I19

8858

,675

103,

548

59,6

2444

,312

New

por t

New

s–H

ampt

on,

VA

1988

80,7

5611

6,35

382

,275

62,2

03N

orfo

lk–V

irgi

nia

Bea

ch–N

ewpo

r t N

ews,

VA

1988

77,3

4211

0,00

178

,682

61,2

95O

kla

hom

a C

ity,

OK

1988

45,8

4176

,304

46,6

5335

,176

Pro

vide

nce

-Paw

tuck

et-W

arw

ick

, R

I-M

A19

8814

1,11

023

4,53

814

1,10

612

7,89

2S

alt

Lak

e C

ity,

UT

1988

62,9

0410

3,19

763

,319

53,7

14S

an J

ose,

CA

1988

211,

582

263,

894

214,

099

187,

439

Bos

ton

, M

A-N

H19

8917

4,26

823

8,21

417

5,60

915

1,64

8D

alla

s, T

X19

8969

,434

110,

759

71,1

7451

,990

Det

r oit

, M

I19

8957

,845

137,

077

58,6

7740

,940

For

t W

orth

, T

X19

8960

,561

102,

536

61,9

9543

,515

Los

An

gele

s–L

ong

Bea

ch,

CA

1989

207,

351

220,

612

213,

346

156,

258

Min

nea

poli

s–S

t. P

aul,

MN

-WI

1989

75,2

7512

4,39

176

,103

61,3

05P

hil

adel

phia

, P

A-N

J19

8993

,762

172,

880

96,1

1264

,089

Ph

oen

ix,

AZ

1989

68,5

7911

8,08

469

,743

52,9

08S

an F

ran

c isc

o–O

akla

nd,

CA

1989

224,

781

258,

013

231,

536

170,

610

Tam

pa–S

t. P

eter

sbu

rg,

FL

1989

56,9

8811

5,71

156

,720

52,3

25W

ash

ingt

on,

DC

-MD

-VA

1989

135,

301

178,

794

136,

182

120,

912


able

4. P

rice

s o

f O

wn

er-O

ccu

pie

d H

ou

sin

g (c

onti

nu

ed)

PO

MS

AP

ON

EW

PO

EX

TP

OS

UB

MS

AS

urv

ey Y

ear

($)

($)

($)

($)

An

ahei

m–S

anta

An

a, C

A19

9015

5,02

022

9,16

114

9,00

920

6,89

7C

inci

nn

ati,

OH

-KY

-IN

1990

62,5

1811

3,81

662

,796

52,9

29D

enve

r, C

O19

9078

,794

133,

657

78,9

7770

,170

Kan

sas

Cit

y, M

O-K

S19

9048

,243

100,

013

48,8

6536

,141

Mia

mi–

For

t L

aude

rdal

e, F

L19

9089

,242

132,

546

90,7

7871

,756

New

Orl

ean

s, L

A19

9068

,452

94,8

1170

,208

50,9

67P

itts

burg

h,

PA

1990

53,3

6311

0,37

154

,552

37,0

08P

ortl

and,

OR

-WA

1990

71,6

3512

6,20

171

,684

62,5

39R

och

este

r, N

Y19

9082

,938

136,

856

83,4

6168

,539

San

An

ton

io,

TX

1990

61,0

2210

2,77

262

,317

44,5

21S

an B

ern

ardi

no–

Riv

ersi

de–O

nta

rio,

CA

1990

125,

319

167,

221

125,

529

112,

032

Atl

anta

, G

A19

9177

,031

113,

590

78,6

0558

,777

Bal

tim

ore,

MD

1991

95,8

1815

5,03

394

,963

88,2

21C

hic

ago,

IL

1991

88,6

0114

0,06

190

,698

63,6

73C

olu

mbu

s, O

H19

9161

,263

96,8

7861

,463

54,0

69H

artf

ord,

CT

1991

150,

049

202,

296

150,

346

138,

590

Hou

ston

, T

X19

9154

,826

86,8

1961

,816

40,1

78N

ew Y

ork

–Nas

sau

–Su

ffol

k,

NY

1991

167,

310

269,

153

169,

119

135,

999

New

ark

–Nor

thea

ster

n N

ew J

erse

y, N

J19

9114

6,58

222

2,61

614

7,57

912

3,86

6P

ater

son

-Cli

fton

-Pas

saic

, N

J19

9119

8,12

838

5,91

019

8,33

015

5,39

7S

t. L

ouis

, M

O-I

L19

9154

,917

95,7

5155

,844

40,1

32S

an D

iego

, C

A19

9120

3,53

725

6,66

720

7,10

416

4,76

6S

eatt

le-E

vere

tt,

WA

1991

150,

078

214,

748

152,

458

120,

981

Tac

oma,

WA

1991

94,1

2316

4,14

495

,261

75,1

71

Bir

min

gham

, A

L19

9255

,399

100,

880

56,8

5838

,323

Cle

vela

nd,

OH

1992

74,5

8113

4,56

175

,205

60,8

49In

dian

apol

is,

IN19

9262

,020

118,

323

62,4

6550

,960

Mem

phis

, T

N-A

R-M

S19

9264

,817

117,

360

66,0

4646

,796

New

por t

New

s–H

ampt

on,

VA

1992

85,9

6612

1,65

487

,919

63,7

28N

orfo

lk–V

irgi

nia

Bea

ch–N

ewpo

r t N

ews,

VA

1992

81,2

1412

1,68

982

,162

66,2

37O

kla

hom

a C

ity,

OK

1992

45,7

8488

,631

46,7

8332

,762

Pro

vide

nce

-Paw

tuck

et-W

arw

ick

, R

I-M

A19

9213

0,81

619

4,65

113

2,58

710

5,50

0S

alt

Lak

e C

ity,

UT

1992

70,2

0911

8,25

670

,882

57,5

44


For a given metropolitan area, let pi,j = Xibj be the (finite-sample-corrected) estimate ofthe price of housing computed using the period i national average bundle of housingcharacteristics and period j metropolitan-area estimated hedonic coefficients. For odd-numbered years, national average housing characteristics are computed from the 1987–91 National AHS. For even-numbered years, the national average characteristics arecomputed by averaging characteristics for adjacent years. The 1993 National AHS wasnot available at the time this work was done, so 1991 average characteristics are used inplace of 1992 average characteristics.

The Laspeyres price index is PL = p0,t /p0,0. The Paasche index is PP = pt,t/pt,0. The Fisherindex is PF = (PLPP)1/2. Annualized housing inflation rates are listed in table 5 (for rentalhousing services) and table 6 (for owner-occupied housing). The first column in thesetables is the MSA, followed by the survey year and the housing bundle year. Estimatedhedonic coefficients are obtained for the survey year, while national average housingcharacteristics are obtained for the housing bundle year. For example, the 1986 Anaheimowner-occupied house price index, computed using 1986 Anaheim hedonic coefficientsand 1986 national average characteristics, is $142,865.30. The 1990 Anaheim owner-occupied house price index, computed using 1990 Anaheim hedonic coefficients and 1986national average characteristics, is $155,062.94. Consequently, the 1986–90 annualizedLaspeyres inflation rate for Anaheim owner-occupied housing is

155 062 94142 865 30 1 2 07

1 4, ., . . %

− = .

The annualized 1986–90 Anaheim Paasche index is computed by pricing 1990 nationalaverage housing characteristics using 1986 and 1990 Anaheim hedonic coefficients. Thepredicted price of the 1990 national average bundle of characteristics using 1986Anaheim hedonic coefficients is $148,091.99, while the predicted price of the identical setof housing characteristics obtained using 1990 Anaheim hedonic coefficients is $154,257.92.Therefore the annualized 1986–90 Anaheim Paasche index is

154 257 92148 091 99 1 1 03

1 4, ., . . %

− = .

The bundle of housing characteristics that is priced clearly influences the house priceindices and the resulting inflation rate: The inflation rate for 1986–90 Anaheim houseprices measured by the Paasche index is half the rate measured by the Laspeyres index.The annualized 1986–90 Fisher index for Anaheim is

1 0207 1 0103 1 1 551 2. . . %×( ) − = .

By the Fisher index, annualized rates of inflation in shelter rents were highest in SanDiego between 1987 and 1991 (8.80 percent) and lowest in Denver between 1986 and 1990(–0.33 percent). The metropolitan-area average annualized rate of inflation in shelterrents over the seven-year period was 4.86 percent. Annualized rates of inflation in theprice of owner-occupied housing were highest in Seattle between 1987 and 1991(16.45 percent), followed by San Diego between 1987 and 1991 (11.67 percent), and lowest


able

5. I

nfl

ati

on

Ra

tes

for

Ren

tal

Ho

usi

ng

Ser

vic

es

Hou

sin

gH

ouse

An

nu

alL

aspe

yres

Paa

sch

eF

ish

erS

urv

eyB

un

dle

Pri

ceC

han

ge i

n Q

Infl

atio

nIn

flat

ion

Infl

atio

nM

SA

Yea

rY

ear

($)

(%)

Rat

e (%

)R

ate

(%)

Rat

e (%

)

An

ahei

m–S

anta

An

a, C

A19

8619

8650

5.41

1986

1990

503.

68–0

.09

1990

1986

664.

037.

0619

9019

9067

1.26

0.27

7.44

7.25

Atl

anta

, G

A19

8719

8729

2.44

1987

1991

297.

660.

4419

9119

8732

9.71

3.04

1991

1991

336.

020.

483.

083.

06B

alti

mor

e, M

D19

8719

8729

3.70

1987

1991

294.

410.

0619

9119

8738

2.71

6.84

1991

1991

383.

560.

066.

846.

84B

irm

ingh

am,

AL

1988

1988

188.

6019

8819

9118

9.03

0.08

1992

1988

217.

643.

6519

9219

9121

8.14

0.08

3.65

3.65

Ch

icag

o, I

L19

8719

8735

6.32

1987

1991

363.

850.

5219

9119

8743

6.96

5.23

1991

1991

438.

960.

114.

805.

02C

inc i

nn

ati,

OH

-KY

-IN

1986

1986

250.

0519

8619

9025

5.77

0.57

1990

1986

282.

543.

1019

9019

9028

8.23

0.50

3.03

3.07

Cle

vela

nd,

OH

1988

1988

307.

1319

8819

9130

7.42

0.03

1992

1988

332.

762.

0219

9219

9133

7.94

0.52

2.39

2.21

Col

um

bus,

OH

1987

1987

251.

2819

8719

9125

5.10

0.38

1991

1987

296.

174.

1919

9119

9130

2.42

0.52

4.35

4.27

Den

ver ,

CO

1986

1986

335.

0319

8619

9033

5.00

0.00

1990

1986

329.

57–0

.41

1990

1990

331.

730.

16–0

. 24

–0. 3

3


able

5. I

nfl

ati

on

Ra

tes

for

Ren

tal

Ho

usi

ng

Ser

vic

es (c

onti

nu

ed)

Hou

sin

gH

ouse

An

nu

alL

aspe

yres

Paa

sch

eF

ish

erS

urv

eyB

un

dle

Pri

ceC

han

ge i

n Q

Infl

atio

nIn

flat

ion

Infl

atio

nM

SA

Yea

rY

ear

($)

(%)

Rat

e (%

)R

ate

(%)

Rat

e (%

)

Har

tfor

d, C

T19

8719

8738

2.44

1987

1991

392.

660.

6619

9119

8748

7.53

6.26

1991

1991

498.

620.

566.

156.

21H

oust

on,

TX

1987

1987

251.

5319

8719

9125

4.22

0.27

1991

1987

286.

423.

3019

9119

9128

9.62

0.28

3.31

3.31

Indi

anap

olis

, IN

1988

1988

251.

7519

8819

9125

6.29

0.60

1992

1988

305.

985.

0019

9219

9130

9.47

0.38

4.83

4.91

Kan

sas

Cit

y, M

O-K

S19

8619

8622

7.42

1986

1990

229.

720.

2519

9019

8627

5.06

4.87

1990

1990

281.

690.

605.

235.

05M

emph

is,

TN

-AR

-MS

1988

1988

247.

8719

8819

9124

9.32

0.19

1992

1988

256.

430.

8519

9219

9126

2.38

0.77

1.28

1.07

Mia

mi–

For

t L

aude

rdal

e, F

L19

8619

8631

9.55

1986

1990

312.

48–0

.56

1990

1986

440.

098.

3319

9019

9044

3.57

0.20

9.15

8.74

New

Or l

ean

s, L

A19

8619

8625

4.61

1986

1990

261.

920.

7119

9019

8628

1.43

2.54

1990

1990

287.

210.

512.

332.

43N

ew Y

ork

–Nas

sau

–Su

ffol

k,

NY

1987

1987

502.

0619

8719

9150

9.88

0.39

1991

1987

640.

476.

2819

9119

9165

0.58

0.39

6.28

6.28

New

ark

–Nor

thea

ster

n19

8719

8742

4.48

New

Jer

sey,

NJ

1987

1991

427.

640.

1919

9119

8753

0.64

5.74

1991

1991

562.

261.

467.

086.

41


able

5. I

nfl

ati

on

Ra

tes

for

Ren

tal

Ho

usi

ng

Ser

vic

es (c

onti

nu

ed)

Hou

sin

gH

ouse

An

nu

alL

aspe

yres

Paa

sch

eF

ish

erS

urv

eyB

un

dle

Pri

ceC

han

ge i

n Q

Infl

atio

nIn

flat

ion

Infl

atio

nM

SA

Yea

rY

ear

($)

(%)

Rat

e (%

)R

ate

(%)

Rat

e (%

)

New

port

New

s–H

ampt

on,

VA

1988

1988

289.

9419

8819

9127

9.69

–1.1

919

9219

8834

2.20

4.23

1992

1991

340.

80–0

.14

5.06

4.65

Nor

folk

–Vir

gin

ia B

each

–19

8819

8830

0.67

New

port

New

s, V

A19

8819

9130

0.38

–0.0

319

9219

8832

2.34

1.76

1992

1991

318.

76–0

.37

1.50

1.63

Ok

lah

oma

Cit

y, O

K19

8819

8818

3.90

1988

1991

182.

61–0

.23

1992

1988

224.

875.

1619

9219

9122

6.28

0.21

5.51

5.33

Pat

erso

n-C

lift

on-P

assa

ic,

NJ

1987

1987

450.

5619

8719

9144

2.91

–0.4

319

9119

8750

9.03

3.10

1991

1991

518.

080.

444.

003.

55P

itts

burg

h,

PA

1986

1986

205.

3219

8619

9020

3.36

–0.2

419

9019

8627

7.77

7.85

1990

1990

283.

780.

548.

698.

27P

ortl

and,

OR

-WA

1986

1986

288.

6019

8619

9029

0.24

0.14

1990

1986

351.

875.

0819

9019

9035

7.52

0.40

5.35

5.22

Pro

vide

nce

-Paw

tuck

et-

1988

1988

401.

38W

arw

ick

, R

I-M

A19

8819

9140

4.11

0.23

1992

1988

425.

371.

4619

9219

9143

2.93

0.59

1.74

1.60

Roc

hes

ter ,

NY

1986

1986

288.

5219

8619

9029

9.68

0.95

1990

1986

372.

266.

5819

9019

9038

4.21

0.79

6.41

6.49


able

5. I

nfl

ati

on

Ra

tes

for

Ren

tal

Ho

usi

ng

Ser

vic

es (c

onti

nu

ed)

Hou

sin

gH

ouse

An

nu

alL

aspe

yres

Paa

sch

eF

ish

erS

urv

eyB

un

dle

Pri

ceC

han

ge i

n Q

Infl

atio

nIn

flat

ion

Infl

atio

nM

SA

Yea

rY

ear

($)

(%)

Rat

e (%

)R

ate

(%)

Rat

e (%

)

St.

Lou

is,

MO

-IL

1987

1987

243.

5319

8719

9124

2.17

–0.1

419

9119

8729

7.62

5.14

1991

1991

300.

660.

255.

565.

35S

alt

Lak

e C

ity,

UT

1988

1988

248.

2619

8819

9125

0.84

0.35

1992

1988

319.

926.

5519

9219

9131

9.98

0.01

6.28

6.41

San

An

ton

io,

TX

1986

1986

207.

6119

8619

9020

6.44

–0.1

419

9019

8625

8.04

5.59

1990

1990

257.

64–0

.04

5.70

5.64

San

Ber

nar

din

o–R

iver

side

–19

8619

8631

3.77

On

tar i

o, C

A19

8619

9031

9.34

0.44

1990

1986

393.

495.

8219

9019

9040

7.60

0.88

6.29

6.06

San

Die

go,

CA

1987

1987

427.

2319

8719

9143

0.78

0.21

1991

1987

607.

189.

1919

9119

9159

5.12

–0.5

08.

418.

80S

eatt

le–E

vere

tt,

WA

1987

1987

377.

2319

8719

9137

5.20

–0.1

319

9119

8747

4.06

5.88

1991

1991

479.

530.

296.

336.

10T

acom

a, W

A19

8719

8730

0.30

1987

1991

308.

670.

6919

9119

8738

2.17

6.21

1991

1991

383.

190.

075.

565.

88A

vera

ge0.

254.

774.

954.

86


able

6.

Infl

ati

on

Ra

tes

for

Ow

ner

-Occ

up

ied

Ho

usi

ng

Hou

sin

gH

ouse

An

nu

alL

aspe

yres

Paa

sch

eF

ish

erS

urv

eyB

un

dle

Pri

ceC

han

ge i

n Q

Infl

atio

nIn

flat

ion

Infl

atio

nM

SA

Yea

rY

ear

($)

(%)

Rat

e (%

)R

ate

(%)

Rat

e (%

)

An

ahei

m–S

anta

An

a, C

A19

8619

8614

2,86

5.30

1986

1990

148,

091.

990.

9019

9019

8615

5,06

2.94

2.07

1990

1990

154,

257.

92–0

.13

1.03

1.55

Atl

anta

, G

A19

8719

8772

,067

.41

1987

1991

72,8

33.0

70.

2619

9119

8777

,051

.83

1.69

1991

1991

78,2

46.7

30.

391.

811.

75B

alti

mor

e, M

D19

8719

8779

,203

.00

1987

1991

80,6

75.6

20.

4619

9119

8796

,989

.79

5.20

1991

1991

99,1

19.6

00.

545.

285.

24B

irm

ingh

am,

AL

1988

1988

50,8

82.1

919

8819

9150

,552

.87

–0.2

219

9219

8856

,254

.47

2.54

1992

1991

56,7

85.8

30.

312.

952.

74C

hic

ago,

IL

1987

1987

81,4

74.5

319

8719

9182

,657

.01

0.36

1991

1987

89,8

25.2

82.

4719

9119

9193

,289

.43

0.95

3.07

2.77

Cin

c in

nat

i, O

H-K

Y-I

N19

8619

8651

,828

.60

1986

1990

51,9

56.0

20.

0619

9019

8662

,447

.79

4.77

1990

1990

63,4

76.1

30.

415.

134.

95C

leve

lan

d, O

H19

8819

8856

,896

.67

1988

1991

57,1

80.4

40.

1719

9219

8874

,095

.36

6.83

1992

1991

74,1

79.1

60.

046.

726.

77C

olu

mbu

s, O

H19

8719

8758

,518

.14

1987

1991

59,0

70.2

00.

2419

9119

8761

,140

.07

1.10

1991

1991

64,0

50.6

11.

172.

041.

57D

enve

r , C

O19

8619

8681

,090

.12

1986

1990

82,6

54.5

70.

4819

9019

8677

,968

.63

–0.9

819

9019

9076

, 471

. 07

–0. 4

8–1

. 93

–1. 4

5


able

6.

Infl

ati

on

Ra

tes

for

Ow

ner

-Occ

up

ied

Ho

usi

ng

(co

nti

nu

ed)

Hou

sin

gH

ouse

An

nu

alL

aspe

yres

Paa

sch

eF

ish

erS

urv

eyB

un

dle

Pri

ceC

han

ge i

n Q

Infl

atio

nIn

flat

ion

Infl

atio

nM

SA

Yea

rY

ear

($)

(%)

Rat

e (%

)R

ate

(%)

Rat

e (%

)

Har

tfor

d, C

T19

8719

8713

7,99

3.91

1987

1991

143,

156.

830.

9219

9119

8715

1,49

8.36

2.36

1991

1991

154,

190.

900.

441.

872.

12H

oust

on,

TX

1987

1987

58,8

71.5

519

8719

9163

,515

.86

1.92

1991

1987

54,5

58.3

1–1

.88

1991

1991

56,5

28.2

40.

89–2

.87

–2.3

8In

dian

apol

is,

IN19

8819

8850

,922

.21

1988

1991

51,0

55.3

90.

0919

9219

8862

,614

.27

5.30

1992

1991

62,8

76.5

10.

145.

345.

32K

ansa

s C

ity,

MO

-KS

1986

1986

45,6

45.1

719

8619

9047

,988

.87

1.26

1990

1986

48,7

17.3

11.

6419

9019

9050

,571

.15

0.94

1.32

1.48

Mem

phis

, T

N-A

R-M

S19

8819

8859

,913

.71

1988

1991

60,5

45.4

90.

3519

9219

8866

,460

.34

2.63

1992

1991

68,6

58.5

71.

093.

192.

91M

iam

i–F

ort

Lau

derd

ale,

FL

1986

1986

80,9

75.6

719

8619

9083

,465

.21

0.76

1990

1986

89,3

30.8

32.

4919

9019

9093

,342

.51

1.10

2.84

2.66

New

Or l

ean

s, L

A19

8619

8667

,214

.53

1986

1990

70,6

17.4

81.

2419

9019

8668

,536

.60

0.49

1990

1990

70,7

40.3

10.

790.

040.

27N

ew Y

ork

–Nas

sau

–19

8719

8716

1,57

0.96

Su

ffol

k,

NY

1987

1991

168,

257.

821.

0219

9119

8717

0,71

3.88

1.39

1991

1991

178,

635.

781.

141.

511.

45N

ewar

k–N

orth

east

ern

1987

1987

161,

066.

55N

ew J

erse

y, N

J19

8719

9115

9,80

4.80

–0.2

019

9119

8714

8,99

6.25

–1.9

319

9119

9114

9,41

9.52

0.07

–1.6

7–1

.80


able

6.

Infl

ati

on

Ra

tes

for

Ow

ner

-Occ

up

ied

Ho

usi

ng

(co

nti

nu

ed)

Hou

sin

gH

ouse

An

nu

alL

aspe

yres

Paa

sch

eF

ish

erS

urv

eyB

un

dle

Pri

ceC

han

ge i

n Q

Infl

atio

nIn

flat

ion

Infl

atio

nM

SA

Yea

rY

ear

($)

(%)

Rat

e (%

)R

ate

(%)

Rat

e (%

)

New

port

New

s–H

ampt

on,

VA

1988

1988

79,2

33.7

019

8819

9177

,763

.34

–0.6

219

9219

8883

,351

.44

1.27

1992

1991

81,3

74.3

1–0

.80

1.14

1.21

Nor

folk

–Vir

gin

ia B

each

–19

8819

8876

,553

.39

New

port

New

s, V

A19

8819

9175

,797

.30

–0.3

319

9219

8881

,397

.39

1.55

1992

1991

81,9

45.6

00.

221.

971.

76O

kla

hom

a C

ity,

OK

1988

1988

46,9

21.5

219

8819

9148

,290

.43

0.96

1992

1988

46,1

50.3

3–0

.41

1992

1991

47,2

78.8

80.

81–0

.53

–0.4

7P

ater

son

-Cli

fton

-Pas

saic

, N

J19

8719

8717

4,97

5.65

1987

1991

182,

165.

961.

0119

9119

8719

8,21

2.08

3.17

1991

1991

201,

566.

200.

422.

562.

86P

itts

burg

h,

PA

1986

1986

48,5

63.2

719

8619

9048

,758

.74

0.10

1990

1986

53,3

95.3

22.

4019

9019

9054

,229

.15

0.39

2.69

2.55

Por

tlan

d, O

R-W

A19

8619

8665

,170

.20

1986

1990

67,6

17.7

10.

9319

9019

8671

,943

.25

2.50

1990

1990

73,2

49.4

90.

452.

022.

26P

rovi

den

ce-P

awtu

cket

-19

8819

8814

3,23

5.07

War

wic

k,

RI-

MA

1988

1991

145,

918.

550.

6219

9219

8813

1,84

5.97

–2.0

519

9219

9113

4,01

4.54

0.55

–2.1

1–2

.08

Roc

hes

ter ,

NY

1986

1986

67,4

04.9

319

8619

9068

,385

.31

0.36

1990

1986

83,2

15.0

35.

4119

9019

9084

,272

.93

0.32

5.36

5.39


Tab

le 6

. In

fla

tio

n R

ate

s fo

r O

wn

er-O

ccu

pie

d H

ou

sin

g (

con

tin

ued

)

Hou

sin

gH

ouse

An

nu

alL

aspe

yres

Paa

sch

eF

ish

erS

urv

eyB

un

dle

Pri

ceC

han

ge i

n Q

Infl

atio

nIn

flat

ion

Infl

atio

nM

SA

Yea

rY

ear

($)

(%)

Rat

e (%

)R

ate

(%)

Rat

e (%

)

St.

Lou

is,

MO

-IL

1987

1987

53,3

83.2

719

8719

9155

,529

.68

0.99

1991

1987

55,9

04.7

61.

1619

9119

9157

,992

.97

0.92

1.09

1.13

Sal

t L

ake

Cit

y, U

T19

8819

8863

,552

.53

1988

1991

64,3

95.7

50.

4419

9219

8871

,254

.67

2.90

1992

1991

72,5

46.3

80.

603.

022.

96S

an A

nto

nio

, T

X19

8619

8662

,250

.55

1986

1990

65,4

53.6

51.

2619

9019

8660

,370

.40

–0.7

619

9019

9059

,019

.90

–0.5

6–2

.55

–1.6

6S

an B

ern

ardi

no–

Riv

ersi

de–

1986

1986

89,9

09.5

1O

nta

r io,

CA

1986

1990

91,3

09.9

80.

3919

9019

8612

5,12

6.13

8.61

1990

1990

127,

238.

960.

428.

658.

63S

an D

iego

, C

A19

8719

8713

1,41

5.87

1987

1991

133,

138.

610.

3319

9119

8720

3,11

9.79

11.5

019

9119

9120

8,23

3.58

0.62

11.8

311

.67

Sea

ttle

-Eve

rett

, W

A19

8719

8782

,275

.42

1987

1991

84,6

25.2

40.

7119

9119

8715

1,48

7.95

16.4

919

9119

9115

5,40

7.42

0.64

16.4

116

.45

Tac

oma,

WA

1987

1987

81,1

15.8

919

8719

9186

,712

.19

1.68

1991

1987

94,2

71.7

23.

8319

9119

9194

,551

.09

0.07

2.19

3.00

Ave

rage

0.51

2.90

2.77

2.84


in Houston between 1987 and 1991 (–2.38 percent). The metropolitan-area averageannualized rate of inflation in owner-occupied housing over the seven-year period was2.84 percent.

A Housing Quantity Index

The differences between the Laspeyres and Paasche indices in tables 5 and 6 suggest thatthe nation’s housing stock is changing over time. A housing quantity index was con-structed to measure the changes. Like a price index, a quantity index is the ratio of two(finite-sample-corrected) expected housing expenditures. For a quantity index, thebeginning and ending periods’ national average housing characteristics are priced usingthe same hedonic coefficients. For each metropolitan area, the quantity index is Q = pt,0/p0,0. Using 1986 Anaheim owner-occupied housing hedonic coefficients with 1990 na-tional average characteristics, the expected expenditure is $148,091.99—an increase of$5,226.69 (0.90 percent per year) over the $142,865.30 expected expenditure obtainedusing 1986 national average characteristics. Because the hedonic coefficients are heldconstant for both expected expenditures, the increase in the index value measures theincrease in the quantity/quality of the stock of housing between 1986 and 1990 (as valuedby 1986 Anaheim hedonic coefficients). The average annual rate of growth in the quantityof the nation’s owner-occupied housing stock, as valued by these metropolitan areas,between 1986 and 1992 is 0.51 percent.

Conclusion

This article reports house price indices for renter- and owner-occupied housing formetropolitan areas surveyed in the 1984–92 MSA AHS. The constant-quality house priceindices are representative of the entire stock of housing within each housing market.Price indices are computed using national average housing characteristics for residentialproperties in places with populations exceeding 100,000. Inflation rates are measuredusing Laspeyres, Paasche, and Fisher indices.

California metropolitan areas typically had the most expensive housing, while MSAs inthe South had the least expensive. Shelter rents in California MSAs were 2.5 times thosein other metropolitan areas. California owner-occupied house prices were 3 times thenational average. The average annualized rate of inflation in rents during the 1986–92period, measured using a Fisher index, was 202 basis points higher than the averageannualized rate of inflation in owner-occupied housing (4.86 versus 2.84 percent).Finally, an index of housing quantity indicates that the nation’s housing stock improvedat an average rate of 0.51 percent per year.

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house price indices from the 1984â€“1992msa american housing surveys

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