A Dynamic Model of Housing Supply

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9 AppendixFurther Estimation DetailsTwo options are available for estimating δ t . The first option would be to assume a transitionprobability for δ t – for example, an AR(1) specification – and use this within the estimationroutine. This would involve guessing a parameter vector ⃗ δ t , estimating (via OLS) first orderautoregressive process transition probabilities, and then evaluating the likelihood using both thechosen parameters and the expected future costs implied by those parameters. This approachwould be most similar to Gowrisankaran and Rysman (2006).The alternative which is used here is to simply estimate year effects to get δ t − βE t δ t+1 .Assuming that transition probabilities are given by an AR(1) process yields a set of uniquevalues for δ t for a given set of autoregressive coefficients. The autoregressive coefficients whichminimize the sum of square residuals using the resulting values for δ t are chosen.To summarize, the steps are outlined below. Using the vector of estimates of δ t − βE t δ t+11) Guess values for α in the equation δ t+1 = α 0 + α 1 δ t + α 2 x t + υ t .2) Solve for ⃗ δ t using α and δ t − βE t δ t+1 .3) Solve for residuals from the equation υ t = δ t+1 − (α 0 + α 1 δ t + α 2 x t ), given x, ⃗ δ t and α.4) Calculate the sum of squared residuals, ssr υ = ∑ Tt=1 υ2 t .5) Choose α to minimize sum of square residuals, ssr υ .51
Page 1 and 2: A Dynamic Model of Housing Supply
Page 3 and 4: period profits by choosing the opti
Page 5 and 6: future price changes and shed light
Page 7 and 8: pany, a consultant to the home buil
Page 9 and 10: Figure 1: Bay Area: Counties and Tr
Page 11 and 12: lands as well as undeveloped farms,
Page 13 and 14: Figure 3: Appreciation Rates by PUM
Page 15 and 16: 4 A Dynamic Model of Housing Constr
Page 17 and 18: expected profits are given by the v
Page 19 and 20: Housing quality is composed of thre
Page 21 and 22: 4.2.2 Optimal Discrete ChoiceThe de
Page 23 and 24: into three pieces:1. L p (θ p , x)
Page 25 and 26: y year. The results section provide
Page 27 and 28: probability of choosing to build. 4
Page 29 and 30: of ̂ε P Cnt , I can calculate ∫
Page 31 and 32: issue arises in Gowrisankaran and R
Page 33 and 34: Figure 5: Distribution of the Price
Page 35 and 36: determining sales price. The result
Page 37 and 38: to identifying costs is different h
Page 39 and 40: for prices and costs as well as an
Page 41 and 42: Figure 12: Geographic Distribution
Page 43 and 44: of dynamics (and in particular, the
Page 45 and 46: price cycle. The expected length of
Page 47 and 48: 8 ConclusionThe importance of the h
Page 49: DiPasquale, D., and W. C. Wheaton (
Page 53: Additional ResultsTable 4: Dynamic

A Dynamic Model of Housing Supply

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