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Suggestions for measuring the effect of distressed sales on assessments

Suggestions for measuring the effect of distressed sales on assessments. Chuck Hicks, RES. Review: How did we get here?. Collapse of the Dot.com bubble, 1999-2000 The Fed began lowering interest rates to stimulate the economy and prevent a recession, 2001

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Suggestions for measuring the effect of distressed sales on assessments

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  1. Suggestions for measuring the effect of distressed sales on assessments Chuck Hicks, RES

  2. Review: How did we get here? • Collapse of the Dot.com bubble, 1999-2000 • The Fed began lowering interest rates to stimulate the economy and prevent a recession, 2001 • Lending practices eased, GSE’s backed risky mortgages • Between 2001-2006, an asset bubble in real estate formed: “malinvestment”

  3. Anatomy of a catastrophe • Consumer spending rose, fueled by low credit card and equity line rates • Developers/builders discovered a lack of capital and loanable funds. Projects were abandoned. • Mortgage defaults rose. The real estate bubble collapsed (2007)

  4. “Nobody saw this coming.” Really? “The day of reckoning for all this mischief is now at hand… Federal Reserve credit has not stimulated economic growth, but a lot of it ended up in the expanding real estate bubble… This, too, will burst as all bubbles do.” ~ Texas Congressman Ron Paul, September 6, 2001

  5. About those “green shoots” • 2 million more homes to go into foreclosure between 2012-2013 • General economic uncertainty = weak seller confidence • Low interest rates reduce sense of urgency among sellers and buyers • Source: Jonathan Miller, Miller Samuel, Inc., (February 2012)

  6. …but what about those “upticks”? “No market trends straight up or straight down.” ~ Doug Casey, Casey Research (2012)

  7. The Assessor’s Dilemma • “When there is a glut of distress sales in the marketplace, and those properties are truly comparable to the subject, it would be misleading not to use them as part (or in some cases all) of the basis for a value conclusion.” • ~ ASB USPAP opinion, June 10, 2011 • G.S. 105-283: the standard definition of market value, “neither (buyer & seller) under any compulsion”

  8. H.R. 1755 • Sec. 3(a)(2)(b) “…use comparable sales involving arms length transactions to make such an assessment or review.” • Sec. 4(2)(B) “…not include any transaction involving a short sale or foreclosed property or any other distressed real property.”

  9. To include or exclude… Excluding distressed sales maintains the letter of G.S. 105-283 and keeps dispersion levels low. But, where distressed activity rises vs. arm’s length sales, “the exceptions become the rule.”

  10. …that is the question. • Including distressed sales in the analysis greatly increases dispersion. • But distressed activity should be thought of as economic obsolescence. Its impact on the market can be measured. • Let us count the ways…

  11. Different strokes… • Ex post facto – market factored adjustments at the end of the review cycle. • e.g. the ASR is 105% at the end of the review. Solution: factor the neighborhood by -5 to -10%. • Downside: property owners perceive this as arbitrary and unfair.

  12. …for statistical folks • The weighted average of distressed vs. arm’s length can be computed. But this requires either constant, real time sales review, or amazing clairvoyance.

  13. Don’t go MIA…try MRA • Multiple regression modeling (MRA) is an handy tool for measuring the impact of distressed sales – without the appearance of arbitrariness • Proxy variables (0, 1) can be assigned to each type of “distress” sale: short, bank, REO, foreclosure • Without delineating the type of distress, the dispersion level in the model will rise

  14. Geospatially speaking… • The best way to improve appraisal results is to link the regression to location, location, location… • “Kriging” – named for South African mining engineer Daniel Gerhardus Krige • Interpolates value of a random field (i.e. price) at an unobserved location from observations at nearby locations

  15. This is so kriging awesome! Before… After…

  16. Advantages • Geospatial modeling reduces the amount of dispersion in the assessment statistics • The distress factor is location-driven, but lacks the appearance of being arbitrary • Requires only periodic uploads of sales data and can be maintained for all future revaluations. As market conditions change, the model seamlessly adjusts. Small jurisdictions can network to create models.

  17. Conclusions • Distressed sales aren’t going away, and the market will “bounce along the bottom” for at least another three years • The problem – economic obsolescence – can’t be ignored, but it can be interpolated into assessments • Geospatial modeling appears to be a sound way to render credible appraisals in the presence of distressed sales

  18. No free lunch…but free software? • “R” is a free, open source statistical program that has a capacity for geospatial modeling. Grab it here: • www.r-project.org • Cran.r-project.org • Pass it to your in-house statistical geek

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