The Urban Housing Market, Structures and Density. | MIT Center for Real Estate Week 3: The Urban Housing Market, Structures and Density. • Hedonic Regression Analysis. • Shadow “prices” versus marginal costs. • Land value maximizing FAR. • FAR and Urban Redevelopment. • Land Use competition: Highest Price for Housing – versus – highest use for land MIT Center for Real Estate Urban Housing • Great diversity from historical evolution, changes in technology and tastes. • Multiple attributes to each house: size, baths, exterior material, style .location • Consumers value each of these attributes with the normal law of micro-economics: diminishing marginal utility. • Huge industry has evolved to applying statistical models to understand and predict diverse house prices: – Property Tax appraisals. – Automatic Valuation Services for lenders, brokers MIT Center for Real Estate Hedonic Regression Analysis 1). Linear: α β β β R = + 1X1 + 2X2 + 3X3 + X’s are structural, location attributes 2). Log Linear: R = e[α + β 1X1 + β 2X2 + β3X3 + ] α β β β ln(R) = + 1X1 + 2X2 + 3X3 + 3). Log Log: α β 1 β 2 β 3 R = X1 X2 X3 α β β ln(R) = ln( ) + 1ln(X1) + 2ln(X2) + MIT Center for Real Estate Dallas apartment rent Hedonic equation: 1998 MIT Center for Real Estate Optimizing House Configuration • Builders and developers compare the incremental value of additional house features against their incremental cost. • Profit maximizing house: where the cost of an additional square foot, bath, fireplace falls to the marginal cost of construction. • But what about land, lot size, density or FAR? – FAR: floor area ratio (ratio of floor to land area). – Density: units per acre. – Density x unit floor area = FAR – % of lot “open” = 1-(FAR/stories) (stories>FAR) MIT Center for Real Estate Optimizing House price (P) minus construction cost (C) as a function of square feet P (size) $ C x Size C ∆P/ ∆Size S* Size (square feet) MIT Center for Real Estate 1). P = α - βF Optimizing FAR α = all housing and location factors .
