Western Economics Association - Forward Valuation Model (Altos Research)
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Western Economics Association - Forward Valuation Model (Altos Research)

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An overview of the Altos Research "Forward Valuation Model" - a home price forecasting model that is built of real-time, local market analytics. Other home price forecasts either suffer from lag......

An overview of the Altos Research "Forward Valuation Model" - a home price forecasting model that is built of real-time, local market analytics. Other home price forecasts either suffer from lag or are too broad geographically for them to be useful for asset-level analysis. Altos Research uses real-time active market data to provide 3-, 6-, and 12-month market forecasts by zip code, price zone, and property type.

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  • 1. Forward Modeling:Knowing Tomorrow’s Housing Market Today July 2, 2011 Scott Sambucci Vice President, Market Analytics scott@altosresearch.com (415) 931 7942
  • 2. Research Question• How to develop a housing market forecasting model applicable to more than 20,000 zip codes across property types and price quartiles?• How to enable regular model revision and updates as new information and data becomes available?
  • 3. Active Market signals future transactionprice Home listed $429,000 Buyer financing fails, Inventory 49 Neighbor home Property relisted listed $394,000 $409,000 Deal closed $389,000 Price reduced $398,000 Inventory 69 Transaction Offer made Recorded $391,000 March May July Sept Nov Jan Closed transaction = 1 data point, months too late Active Market = 9 months of pricing, price changes, supply and demand, leading indicators
  • 4. Sample Sizes: Transactions vs. Actives
  • 5. Lead the Headlines by 3 months
  • 6. Housing Market News: Release &Report Dates Source Inflection Date Data Published Altos Research Q3-2010 Webcast Jul-10 14-Jan-11 17-Jan-11 Altos 20-city Composite Price of New Listings (weekly) Altos 20-city Composite Median Price (weekly) 4-Feb-11 7-Feb-11 Altos 20-city Composite Median Price 25-Mar-11 28-Mar-11 (90 Day Rolling Avg) CoreLogic HPI Apr-11 1-Jun-11 FHFA national home price index Apr-11 22-Jun-11 Radar Logic RPX Apr-11 23-Jun-11 S&P Case Shiller 20 City Composite Apr-11 28-Jun-11
  • 7. Importance of Active Market Indicators• ANGLIN, RUTHERFORD & SPRINGER (2003), “The Trade-Off Between the Selling Price of Residential Properties and Time-on-the-Market: The Impact of Price Setting,” JJREFE• MILLER & SKLARZ (1987), “Pricing Strategies and Residential Property Selling Strategies,” JRER• SPRINGER(1996), “Single-family housing transactions: Seller motivations, price, and marketing time,” JREFE• YAVAS & YANG (1995), “The Strategic Role of Listing Price in Marketing Real Estate: Theory and Evidence,” REE• KANG & GARDNER (1989), “Selling Price and Marketing Time in the Residential Real Estate Market,” JRER
  • 8. Published research & models limited bylocal data sets & time series• Boston: GENESOVE & MAYER (2001). “Loss Aversion and Seller Behavior: Evidence from the Housing Market,” QJE• Stockton, CA: KNIGHT (2002), “Listing Price, Time on Market, and Ultimate Selling Price: Causes and Effects of Listing Price Changes,” REE• Arlington, TX: ANGLIN, RUTHERFORD, & SPRINGER (2003), “The Trade- Off Between the Selling Price of Residential Properties and Time-on- the-Market: The Impact of Price Setting,” JREFE• Columbus, OH: HAURIN, et al (2006), “List Prices, Sale Prices, and Marketing Time: An Application to U.S. Housing Markets,” Working Paper
  • 9. The Data• 400 individual statistics & leading indicators updated weekly for 20,000 zip codes based on the active market• Independently calculated by property type (single-family & condo) and price range quartile• Primary data with uniform methodology for all statistics
  • 10. Model DevelopmentStep 1: Traditional OLSObjective: Build models by zip code for 20k zipsProcess: Test set  Limited OLS ModelsOutcome: Variables & coefficients changed drastically from market to marketStep 2: Regression Trees (CART)Objective: Increase accuracy from OLSProcess: Test set  Built models for 20k zipsOutcome: No coefficients, Trees randomly generated, Interpretability problemsStep 3: Least Angle Regression (LARS)Objective: Increased transparencyProcess: Test set  Build models for 20k zipsResult: Linear model with Coefficients, Transparent, Interpretable
  • 11. What’s next?• Introduce non-linearity – Add quadratic basis functions to capture economic growth• Introduce related variables – Mortgage rates – Macroeconomic indicators – Rental rates – REO/Distressed market-specific
  • 12. ContactScott SambucciVice President, Market Analytics(415) 931 7942scott@altosresearch.com@scottsambucciwww.altosresearch.comblog.altosresearch.com