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Study of Real Estate Pricing

TEAM MEMBERSGhazaleh HassanzadehHuzair Tirmizi Golnaz ShahfipoufardNitin MauryaTanu Aggarwal

DEMAND & REVENUE MANAGEMENT PROJECT

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AGENDA

Objective US Housing Market Collin County Housing Market Demand Drivers Research Methodology Regression Analysis Observations Zillow v/s leading Competitors Future Scope Q & A

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OBJECTIVE

The focus of this project is to understand the dynamics of pricing a house

We studied major factors that can be quantified and could affect the pricing of a house

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THE U.S. HOUSING MARKET

“Boom-Bust" cycles

The U.S. housing market went bust beginning in 2006

The U.S. housing prices may post increases of 1 to 2 percent annually through 2020

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COLLIN COUNTY HOUSING MARKET

Resurging Housing market with rapid growth Increasing demand

• Average days on the market• Month’s supply of inventory

Increasing Price• Increasing trend of average original list prices of homes

Decreasing foreclosure• Foreclosures have decreased dramatically over the past three years

Total estimated valuations for 2015 have reached almost $100 billion with 11 percent jump from last year.

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COLLIN COUNTY HOUSING MARKET

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COLLIN COUNTY HOUSING MARKET

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COLLIN COUNTY HOUSING MARKET

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DEMAND DRIVERS

Market Size • Population has risen• Collin County’s unemployment rate has reduced (5.9% to 3.3%)• 42.60% Future job growth (Plano)

Income/Wealth • Median household income for Collin County is $81,819• Median household income for Plano is $95,150

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DEMAND DRIVERS

Prices of Substitutes • Renting becomes more expensive to owning a house• Zillow rent versus buy calculator: better to buy than rent if

living in home more than 1 year and 11 months • Super low Mortgage rates

Expectations• Expectations of higher prices or rents in the future• Growth expectations

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Choose houses from Zillow Parameters considered: Property ID, selling price, floor-size-SQFT,

lot-area-SQFT, price/SQFT, number of rooms, number of baths, age (month)

Track and collect data in MS Excel for 40 houses over 1 month Regression Analysis done in BI package: SAS Test the Hypothesis Test the accuracy of Regression Equation

RESEARCH METHODOLOGY

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ZILLOW WEBSITE

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REGRESSION ANALYSIS

Regression Equation:

• Selling_Price (House) = β1 + β2 *Floor_Size_sqft + β3* Lot_Area_sqft + β4* Pricepersqft + β5* No_of_Rooms + β6* No_of_Baths + β6* Age_months

Primary Hypothesis:

• Ho: If there is NO significant effect of independent variables on dependent variable?

β1= β2= β3=0• H1: If there is any significant effect of independent variables on dependent

variable? β1≠ β2≠ β3≠0

Secondary Hypothesis:

• Ho: The coefficient of the respective parameters is zero.

βi=0 for all i = 1 to 6• H1: The coefficient of the respective parameters is significantly different from zero.

βi≠0 for all i = 1 to 6

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SAS OUTPUT

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SAS ANALYSIS

p-value: <.0001 hence, we REJECT our Ho • Conclusion: There is significant effect of independent variables on

dependent variable.

R-Squared: 97.6% • Proportion of variance in Selling Price that can be explained by the

independent variables: Floor Size, Lot Area, Price per sqft, No. of Rooms, No. of Baths and Age in months.

Quantitative Effects of the Model • If floor size increases by 1 sq.ft., Selling Price of the house increases by

$119.5. • If price per sq.ft. increases by $1, Selling Price of the house increases

$3548.• If Age of house was 1 month older then the value of the selling price of

the house increases by $150.

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PREDICTIVE ABILITY OF THE MODEL

http://www.zillow.com/homes/recently_sold/Plano/TX/65699878_zpid/53915_rid/33.175491,-96.513348,32.946741,-96.959668_rect/11_zm/?3col=true

Estimate as per Regression Equation: $461,549 Selling Price by Zillow: $467,500

Attribute ValueAddress 7017 Brook Forest Cir Plano, Texas - 75024

Floor Size 3324 ft.

Lot Area 7187 Sq. ft.

Price per sqft $ 141/ ft.

No of Rooms 4 Beds

No of Baths 4 Baths

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OBSERVATIONS

Prices in Collin County increasing in near future Exogenous factors affecting Real Estate pricing in Collin County:

• Market Size • Income • Price of Substitutes• Customer Expectations

Most Significant factors affecting house price • Floor Size• Price / Sq. ft.• Age of house

97.6 % variance in Selling Price is explained by independent variable.

98.7 % accuracy achieved with our current regression model

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ZILLOW V.S LEADING COMPETITORS

9/ 20 times Zillow within +/- 5% Range

8/ 20 times Trulia overestimates

Websites

Attributes Listed

Sold Price

Floor Size

Lot Area

Price/ Sq. ft.

No. of Rooms

No. of Baths

Built In

Zillow x x x x x x x Movoto x x

x x x

Trulia

x

x x x x

Realtor

x x x x x x RealtyTrac x x x x x x x

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FUTURE SCOPE

Include more parameters like garage, Swimming pool etc. to get more sophisticated Regression Analysis

Combine with macroeconomic and demographic factors to forecast the price of house.

Comparison between 2 counties of same state or different states to study which factors impacts more in which area.

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SUMMARY

Plano is an upcoming market to buy house• Population • Unemployment• Future Job Market• High Median Income• Renting becoming expensive.

What affects the price of house• Macroeconomic, Employment, Demographic factors

How to estimate the price of house with 98.7% accuracy Where to find the best estimates of your house

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THANK YOU