Dynamics of pricing a house in Real Estate market
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A Study under Prof. Metin Cakanyildirim to understand the various factors involved in pricing of house and perform a Regression Analysis to understand their impact.
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Dynamics of pricing a house in Real Estate market
- 1. 1 Study of Real Estate Pricing TEAM MEMBERS Ghazaleh Hassanzadeh Huzair Tirmizi Golnaz Shahfipoufard Nitin Maurya Tanu Aggarwal DEMAND & REVENUE MANAGEMENT PROJECT
- 2. 2 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
- 3. 3 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
- 4. 4 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
- 5. 5 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.
- 6. 6 COLLIN COUNTY HOUSING MARKET
- 7. 7 COLLIN COUNTY HOUSING MARKET
- 8. 8 COLLIN COUNTY HOUSING MARKET
- 9. 9 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
- 10. 10 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
- 11. 11 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
- 13. 13 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
- 14. 14 SAS OUTPUT
- 15. 15 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.
- 16. 16 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 Value Address 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
- 17. 17 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
- 18. 18 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
- 19. 19 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.
- 20. 20 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
- 21. 21 THANK YOU
Hinweis der Redaktion
- Pricing of a house is dependent on various variables (external and internal), for e.g. No of Bedrooms, Area, Location etc. In our project, we studied some major factors that could be quantified and could affect the pricing of a house: • Price • No. of Bedrooms • No. of Bathrooms • Lot Area • Sq. ft. • Age And we tried to find a relation of these factors with Price of houses.
- The U.S. housing market is one that's characterized by what's called "boom-bust" cycles. In a housing boom-bust cycle, prices for housing rise for a time, sometimes steeply, and then decline, also sometimes steeply. U.S. housing prices typically rise and fall, only to rise and then fall again, over and over. The U.S. housing market went bust beginning in 2006 due to an economic slowdown. As more people became unemployed, their mortgages became too much to afford, and they defaulted. When mortgage defaults increased, lenders began suffering huge losses on mortgage loans they'd given. Lenders then reacted to mortgage defaults by tightening credit, thus making eligible homebuyers scarcer and driving down home prices as a result. Even with millions of homes available for sale, there were relatively few eligible buyers, depressing listing prices even more. U.S. housing prices are starting to see at least modest annual increases. According to A study in University of California, housing prices may post modest increases of 1 to 2 percent annually through 2020. However, individual markets are expected to experience steeper rises than the expected annual increase of 1 to 2% in housing prices. In housing, location is everything, and prime real estate locations will surly see greater price increases. Last but not least, real estate markets are cyclical, with boom-bust cycles a regular event, meaning within 15 or so years another real estate boom may occur.