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Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
Forward Valuation Model: Knowing Tomorrow's Housing Prices Today
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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  • 1. Forward Modeling:Knowing Tomorrow’s Housing Market Today July 2, 2011<br />Scott Sambucci<br />Vice President, Market Analytics<br />scott@altosresearch.com<br />(415) 931 7942<br />
  • 2. Research Question<br />How to develop a housing market forecasting model applicable to more than 20,000 zip codes across property types and price quartiles?<br />How to enable regular model revision and updates as new information and data becomes available?<br />
  • 3. Active Market signals future transaction price<br />Home listed<br />$429,000<br />Inventory 49<br />Buyer financing fails,<br />Property relisted $394,000<br />Neighbor home listed<br />$409,000<br />Deal closed<br />$389,000<br />Price reduced<br />$398,000<br />Inventory 69<br />Transaction Recorded<br />Offer made<br />$391,000<br />March<br />July<br />Nov<br />Jan<br />May<br />Sept<br />Closed transaction = 1 data point, months too late<br />Active Market = 9 months of pricing, price changes, supply and demand, leading indicators<br />
  • 4. Sample Sizes: Transactions vs. Actives<br />
  • 5. Lead the Headlines by 3 months<br />
  • 6. Housing Market News: Release & Report Dates<br />
  • 7. Importance of Active Market Indicators<br />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<br />MILLER & SKLARZ (1987), “Pricing Strategies and Residential Property Selling Strategies,” JRER<br />SPRINGER(1996), “Single-family housing transactions: Seller motivations, price, and marketing time,” JREFE<br />YAVAS & YANG (1995), “The Strategic Role of Listing Price in Marketing Real Estate: Theory and Evidence,” REE<br />KANG & GARDNER (1989), “Selling Price and Marketing Time in the Residential Real Estate Market,” JRER<br />
  • 8. Published research & models limited by local data sets & time series<br />Boston: GENESOVE & MAYER (2001). “Loss Aversion and Seller Behavior: Evidence from the Housing Market,” QJE <br />Stockton, CA: KNIGHT (2002), “Listing Price, Time on Market, and Ultimate Selling Price: Causes and Effects of Listing Price Changes,” REE<br />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<br />Columbus, OH: HAURIN, et al (2006), “List Prices, Sale Prices, and Marketing Time: An Application to U.S. Housing Markets,” Working Paper<br />
  • 9. The Data<br />400 individual statistics & leading indicators updated weekly for 20,000 zip codes based on the active market<br />Independently calculated by property type (single-family & condo) and price range quartile<br />Primary data with uniform methodology for all statistics<br />
  • 10. Model Development<br />Step 1: Traditional OLS<br />Objective: Build models by zip code for 20k zips<br />Process: Test set  Limited OLS Models <br />Outcome: Variables & coefficients changed drastically from market to market<br />Step 2: Regression Trees (CART)<br />Objective: Increase accuracy from OLS<br />Process: Test set  Built models for 20k zips<br />Outcome: No coefficients, Trees randomly generated, Interpretability problems<br />Step 3: Least Angle Regression (LARS)<br />Objective: Increased transparency<br />Process: Test set  Build models for 20k zips<br />Result: Linear model with Coefficients, Transparent, Interpretable<br />
  • 11. What’s next?<br />Introduce non-linearity<br />Add quadratic basis functions to capture economic growth<br />Introduce related variables<br />Mortgage rates<br />Macroeconomic indicators<br />Rental rates<br />REO/Distressed market-specific<br />
  • 12. Contact<br />Scott Sambucci<br />Vice President, Market Analytics<br />(415) 931 7942<br />scott@altosresearch.com<br />@scottsambucci<br />www.altosresearch.com<br />blog.altosresearch.com<br />

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