Was the Foreclosure Crisis a Supply Problem?

Megan Kirkeby

GIS Final

12/7/2010

• In my midterm presentation I was able to show which parts of Los Angeles County were most affected by the foreclosure crisis of the late 2000’s.

• For this final presentation I would like to address one of the possible causes of the crisis; a lack of choice for consumers.

• I will suggest a lack of affordable rental housing options, low interest rates, and low gas prices led many people to seek seemingly affordable homeownership options further from job centers.

• I will also offer suggestions for further data collection.

Before the Housing Boom…

• We can use the 2000 census to create a snapshot of the housing need before the housing boom years (2002-2005).

• We will examine median rents, rent burden, vacancy rates, and commute times to form an idea of where additional housing stock would have been most valued.

The green areas are the only places where median rents are affordable to minimum wage workers.

Hot spot analysis from index of median rent, percent of income going toward rent, and vacancy rate. Demonstrates areas that would benefit from additional multi-family housing.

Where did the latest production go and what did it look like?

• During the early to mid 2000’s there was a sharp increase in housing production, but much of it was single family home production away from job centers.

• It is difficult to track exactly where those homes were built since the majority went to Los Angeles City or Unincorporated Areas of Los Angeles County which are both spread out over a large area.

Source: Rand Institute

Source: Rand Institute

Was the housing boom’s production mainly single or multi-family home

production?

Data Source: CA Dept. of Housing and Community Development (Raising the Roof) and the Construction Industry Research Board Graph by Megan Kirkeby

California needs 220,000 new homes and apartments each year to keep pace with population growth. In the 1980s, California saw 94% of its housing need being met, in the 1990s that

dropped to 50%. Multifamily housing production suffered the most; less than 30% of total new construction was multifamily apartments, down from nearly 67% in 1970.

Data Source: California Building Industry Association Graph by Megan Kirkeby

Building in California has traditionally focused on Single Family home development. This was exacerbated during the housing boom. This increased the

available housing stock, but primarily for middle and upper income individuals.

Why the sharp increase in Single Family Home Production?

• As the next slides will show during the housing boom we saw low interest rates. This may have encouraged former renters to seek out mortgages as a means of seeking affordable housing.

• Initially the decade also saw low gas prices which may have persuaded people that living further from work would be an acceptable sacrifice for affordable homeownership.

• Additionally, barriers to infill development make it more appealing to building further away from urbanized areas.

Interest Rates for Conventional 30 Year Mortgages (1980-2009)

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

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16.00%

18.00%

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Year

Ave

rage

Inte

rest

Rat

e

Data Source: Federal Reserve Board Graph by Megan Kirkeby

Housing BoomHousing BoomHousing BoomHousing Boom

Annual Motor Gasoline Retail Price

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0.50

1.00

1.50

2.00

2.50

3.00

3.50

1980 1985 1990 1995 2000 2005

Dollars per gallon

Nominal Price

Real Price (Dec 2010 $)

Data Source: US Energy Information Administration Graph by Megan Kirkeby

Housing Boom

Annual Motor Gasoline Retail Price

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1980 1985 1990 1995 2000 2005

Dollars per gallon

Nominal Price

Real Price (Dec 2010 $)

Housing Boom

Conclusions and Data Dreams• While the data is as to how Los Angeles County responded to its

housing demand during the housing boom, it is clear that state wide there has been an emphasis on single family production which is not an affordable option for many consumers.

• This project would benefit from addresses of new permits to track locations and type of production to see how it overlays with the Housing Demand Index.

• The Housing Demand Index shows high demand near the urban core, and strongly advocates for encouraging infill development. However, this project would benefit strongly from Job Center statistics (i.e. areas rich in jobs) as an additional source for housing demand.

• When the 2010 Census Data is released in January it will be exciting to see how rents and development patterns have changed on a census tract level in the last ten years.

Skills Used:• Slide 4: Inset map, overlaying layers.• Slide 5: Created a new data set from median rents and the LA County minimum wage

($8/hour) to create an affordability rating.• Slide 6: Showed vacancy rates by dividing number of vacant rental units by total rental

units in the field calculator.• Slide 7: Created new data set of Long Commuters from Census information on number of

commuters tracked by length of commute. I divided number of workers with oneway commutes over 60 minutes to track percent of workers with Long Commutes.

• Slide 8: Hot Spot Analysis for Housing Demand Index (originally included percentage of rent spent on income, but after doing the raster calculation with and without it I decided this would not contribute to Housing Demand analysis). See models for rasterizing and reclassifying on next slide. Also, created inset maps to the highest demand areas.

• Slide 9: Here I had to create a new data set with only the census tracts capturing the highest housing demand. From there I added centroids to those tracts and added a 5 mile buffer thus using a measuring tool. This captures good areas to add affordable housing. This was also my metadata set (the buffers).

• Slide 11: Added up development from 2001-2005 and development from 1996-2000 so that it was possible to show percentage increase in home development rather that simple totals. This was done using the field calculator.

• Slide 13, 14, 16, 17: Charts to assist in “storytelling” created in excel.

Models

Meta Data