Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
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Forward Valuation Model: Knowing Tomorrow's Housing Prices Today

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Presentation provided by Scott Sambucci at the annual Western Economics Association meeting in July 2011. This presentation was part of a session organized by Andrew Leventis and Jesse Caldwell ...

Presentation provided by Scott Sambucci at the annual Western Economics Association meeting in July 2011. This presentation was part of a session organized by Andrew Leventis and Jesse Caldwell Weiher from the Federal Housing Finance Agency - "Forecasting Short- and Medium-Run Trends in Home Prices." Contact Scott Sambucci directly with any questions - scott@altosresearch.com | (415) 931 7942.

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Forward Valuation Model: Knowing Tomorrow's Housing Prices Today Forward Valuation Model: Knowing Tomorrow's Housing Prices Today Presentation Transcript

  • Forward Modeling:Knowing Tomorrow’s Housing Market Today July 2, 2011
    Scott Sambucci
    Vice President, Market Analytics
    scott@altosresearch.com
    (415) 931 7942
  • 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?
  • Active Market signals future transaction price
    Home listed
    $429,000
    Inventory 49
    Buyer financing fails,
    Property relisted $394,000
    Neighbor home listed
    $409,000
    Deal closed
    $389,000
    Price reduced
    $398,000
    Inventory 69
    Transaction Recorded
    Offer made
    $391,000
    March
    July
    Nov
    Jan
    May
    Sept
    Closed transaction = 1 data point, months too late
    Active Market = 9 months of pricing, price changes, supply and demand, leading indicators
  • Sample Sizes: Transactions vs. Actives
  • Lead the Headlines by 3 months
  • Housing Market News: Release & Report Dates
  • 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
  • Published research & models limited by local 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
  • 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
  • Model Development
    Step 1: Traditional OLS
    Objective: Build models by zip code for 20k zips
    Process: Test set  Limited OLS Models
    Outcome: Variables & coefficients changed drastically from market to market
    Step 2: Regression Trees (CART)
    Objective: Increase accuracy from OLS
    Process: Test set  Built models for 20k zips
    Outcome: No coefficients, Trees randomly generated, Interpretability problems
    Step 3: Least Angle Regression (LARS)
    Objective: Increased transparency
    Process: Test set  Build models for 20k zips
    Result: Linear model with Coefficients, Transparent, Interpretable
  • 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
  • Contact
    Scott Sambucci
    Vice President, Market Analytics
    (415) 931 7942
    scott@altosresearch.com
    @scottsambucci
    www.altosresearch.com
    blog.altosresearch.com