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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.

Published in: Business
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Dynamics of pricing a house in Real Estate market

  1. 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. 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. 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. 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. 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. 6 COLLIN COUNTY HOUSING MARKET
  7. 7. 7 COLLIN COUNTY HOUSING MARKET
  8. 8. 8 COLLIN COUNTY HOUSING MARKET
  9. 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. 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. 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
  12. 12. 12 ZILLOW WEBSITE
  13. 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. 14 SAS OUTPUT
  15. 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. 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. 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. 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. 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. 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. 21 THANK YOU

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