Readings. Quadrant III: The Supply of New Construction. Demand Shocks with Elastic Supply

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1 Readings Quadrant III: The Supply of New Construction Required: Green and Malpezzi, A Primer on U.S. Housing Markets and Housing Policy, pp (on measuring prices), and pp (on measuring regulation). Background: Davis, Lehnert and Martin, The Rent Price Ratio for the Aggregate Stock of Owner Occupied Housing. Review of Income and Wealth, Background: Green, Malpezzi and Mayo, Metropolitan specific estimates of the price elasticity of supply of housing, and their sources. American Economic Review, Background: Malpezzi and Maclennan, The Long run Price Elasticity of Supply of New Residential Construction in the United States and the United Kingdom. Journal of Housing Economics, Background: David Shulman, The Outlook for Commercial Real Estate: Asset Prices Ahead of Fundamentals. UCLA Anderson Forecast, June Demand Shocks with Inelastic Supply Demand Shocks with Elastic Supply Rent (or Price) per Unit of Space Supply Rent (or Price) per Unit of Space Supply P 0 P 0 Q 0 Demand Falls Demand Increases Demand (original) Number of Housing Units Q 0 Demand Falls Demand Increases Demand (original) Number of Housing Units

2 Elasticity Economic jargon for "responsiveness." The proportionate change in output given a proportionate change in price. Mathematical representation of price elasticity of supply: Δ P There are many elasticities, e.g. supply vs. demand elasticities; w.r.t. price, income, population Be familiar with this and with the corresponding graphical representation. Be familiar with how elasticity varies with, inter alia, time, and the extent (size) of the market.δq/qεpsss/p Calibrating Quadrant III: empirical estimates of the supply determinants of commercial real estate are scarce! There are now a few dozen good studies of (e.g.) the price elasticity of supply of housing. See Green, Malpezzi and Mayo (2005), Malpezzi and Maclennan (2001), and references therein. It s hard to think of a single good estimate of the price elasticity of supply of, say, office space. How steep is the supply curve? We ll look at housing first; then nonresidential supply

3 What s the price of housing, anyway? Every house is more or less unique: Size Quality Services Location, location, location If I pay $1,000/ month rent and you pay $1,200, who s paying a higher price? If our units are nearly identical, you do. If your apartment is 1500 square feet and mine is 500, maybe I do. If your apartment is in midtown Manhattan and mine is in Madison, maybe I do. How do we handle many characteristics at once? Simple medians and averages These have one great virtue: simplicity. They also tell us something about levels as well as changes. No/few controls for (e.g.) size, quality differences. These matter more for longer time spans. These matter more when the market turns weird. These matter more when locations have very different housing stocks. Examples: National Association of Realtors median sales price of existing homes. Hedonic price indexes: regression analysis and valuation Hedonic models adjust for the quality of housing services from a given unit. Needed: data on properties that have sold, and their characteristics. Hedonic price indexes can be constructed from place to place, and/or over time; can estimate price levels as well as price changes. Regression Analysis and Valuation ln(sales Price) Xβ Nα Lλ Cγ ε ^ V e ^ ^ ^ ^ Xβ Nα L λ C γ Where X are structural characteristics, N are neighborhood characteristics, L is location, and C are contract conditions. These regression models are called hedonic price indexes by economists. Example: See Primer, Table 2.5, metro data for Washington, D.C. 3

4 Repeat sales price indexes Repeat sales indexes are becoming popular for estimating price changes, if appropriate data are available. Case Shiller price indexes OFHEO price indexes (from Fannie and Freddie data) Needed: data on sales prices of units that sell at least twice. These must be arms length transactions. These must be units that have not depreciated much (or an adjustment must be made). These must be units that have not been upgraded (or an adjustment must be made). User cost Determine what a user of the house really pays (or would pay), net of financing, taxes, maintenance, inflation, and so on. User cost measures are most often time series but can be cross section. Figure 2.14 $1,000,000 Natural Logarithm of Sale Price $100, Example of Repeat Sales Sample 17 Properties that Sold Twice Spliced Quarterly Real House Price Index FHFA Index 1975 to 1986; Case Shiller $10,000 Mar-86 May-87 Jun-88 Jul-89 Aug-90 Sep-91 Oct-92 Source: Village of Shorewood Hills, WI. Dates of Sale Slope of line is rate of appreciation Spliced FHFA/CS Real House Price Index Prediction from Log Trend, Prediction, minus 2 SEs Prediction, plus 2 SEs 4

5 Davis Estimates of Gross Rent Price Ratio, U.S. Stock of Owner Occupied Housing Based on Federal Housing Finance Agency Prices Based on Case Shiller Weiss Prices Housing problems simplified Every country has only two housing problems, in the end. And once you fix those two problems, everything else falls into place. The difficulty is, these are the two housing problems: Housing prices are rising too fast; and Housing prices are not rising fast enough. Larry Hannah, former World Bank Lead Economist, Urban Development. 160 Nobody buys a house in the United States What does it mean to look at a national price index, anyway? You buy a house in a metropolitan area

6 500 Inflation Adjusted FHFA House Price Indexes Selected Midwest Markets 500 Inflation Adjusted FHFA Housing Price Indexes Twelve Most Volatile Markets (thru Q1 2010) Milwaukee Madison Average of 147 MSAs Chicago Minneapolis St Louis Cincinnati Cleveland Detroit Ann Arbor Nassau Salinas Boston Santa Cruz Napa, CA New York San Luis Obispo San Jose San Francisco Peabody Cambridge Santa Barbara Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Inflation Adjusted OFHEO House Price Indexes Twelve Least Volatile Markets (through Q1 2010) 166 Topeka Fort Worth Rockford Wichita Little Rock Fort Wayne Dallas Memphis Lincoln Indianapolis Beaumont Tulsa Median House Value, 1990 Census (Thousands) House Prices & Regulation HRT PRV CHI BAL ALN ALB PHL DAL MIN DEN PHX ROC ATL FTL MIA MIL STL DET BUF RCH ORL TPA NO POO COL CLE KCM MEM HOU IND CTE DTN GRRGNC SYR SLK CIN GRY OKC SAT TUL BIR AKR MOB TDO YNG PGH LA NY NWK BOS SDI MSA-Specific Regulatory Index Source: Malpezzi, J. Hsg Research, 1996 Summarized in Primer, pp SJS SAC SF HON What s 167 this? 6

7 What does it mean to measure regulation? Building permit caps, sewer moratoria, large lot zoning, minimum lot sizes, floor area ratio, setback requirements, height limits, subdivision codes, environmental impact reviews, transferable development rights, retention ponds, rent controls, land use controls, impact fees, building codes, minimum street widths, curb and gutter requirements, traffic mitigation Malpezzi and Ball, Malpezzi (1996), Gyourko, Saez and Summers (2006): regulations are correlated. Implies that omitted regulations will be picked up by included regulations. Implies that we can t take the partial derivative of an element of a regulatory index. How do we interpret MSA indexes of regulation when many sub MSA jurisdictions have regulatory authority? Measuring Regulation by MSA Index based on answers to the following: Recent changes in approval time for single family housing development Time required for rezoning and permitting for a small residential subdivision Ditto, for a large subdivision Single family zoning, compared to demand Multifamily zoning, compared to demand Percent of zoning changes approved Index of adequate infrastructure (roads and sewers) Index values range from 7 (permissive) to 35 (most stringent) Other indexes constructed for state regulations, rent control. 169 Supply from new construction: divergent views Studies by Richard Muth, Jim Follain: the long run price elasticity of supply of housing from new construction is high (10, 20 or more). Studies by Jim Poterba, Topel and Rosen: the price elasticity of supply is more like 2 or 3. Malpezzi and Maclennan: the U.S. elasticity is high; Poterba and Topel and Rosen don t use enough data. But the long run is, well, long. Green, Malpezzi and Mayo, and Tsur Somerville and Chris Mayer: the supply elasticity varies a lot by MSA. Appears to be related to regulation. Supply Elasticity, Lagged RHS Vars CHI Housing Supply Elasticities and Development Regulation DAL STL MIN TPA DEN DET BUF NO PHX OKC GRR SAT MIL KCM POO SLK HRT MSA Regulatory Index What s this? Source: Green, Malpezzi & Mayo (Linear Fit) 171 ATL COL TUL ROC BAL BIR ORL PRV HOU IND AKR MEM SYR CTE CIN FTL TDO PGH ALB PHL MIA LA SJS SDI BOS HON SF 7

8 0.15 Office Appreciation and Regulation 1989 to LA Annual Appreciation JKL CHI DET TPA STL CIN BAL CLE PHL DAL INDPOO RVR NO SEA HOU PHX KCM OKC MIL PGH BIRATL SAC NSH COL FTL GNC CTE MIN LSVORL SLK SAT DEN AUS SDI NFK NY WPB BOS NAU NWK SJS MIA SF HON MSA Regulatory Index 172 (Linear Fit) Regulation raises real estate prices; is that a good thing for investors? What s the other thing that accompanies high returns? 174 8

9 Std Dev Real Agency House Price Change 18% 16% 14% 12% 10% 8% 6% 4% Std Dev of Real Avg House Price Change ( ) and Regulation (1989) Exploratory Regression, Explaining Standard Deviation of Annual Agency Housing Price Changes, U.S. Metro Areas FWA RDG LA APL SRS SF SIL RVR SAT MOD SMA BEL SDI PRV DAV RAC SAC SEA HRT PEO LAN ALN ALB NY SBR NWK NAU BOS RKF VAL PME MIL AUS STC ANN SNS SCZ OKC SYR TDO SLK DES LVL POO PGH CDRPHL BUF DTN HOU CHI MEM GBY TUC GRY LAN EUG WIL CSC BILPRO FCL SAG GSC LSV DET DAL AKR CLE NO TUL ABQ BIR ROC WCH BATDEN BAL PHX NSH KALCOS MAD GRR INC CTN FRO YRK MEL TAC SLMWPB DAB KCM CIN LRA FTL HAL EVN HMOHBG KNX VIS LEXNFK MIA STL IND COL BAK CSC OMH ATLRCH CTE RLG SRA AUG FLT MIN GNC RNO JKL ORL TPA 2% Malpezzi Chun & Green Regulatory Index NHA TRN ATC SJS Std Dev of Real Changes in Income Per Capita Std Dev of Annual Changes in Employment M-C-G Regulatory Index Standardized t-statistic Prob > t Coefficient Intercept Adjusted R-squared: 0.21 Degrees of freedom: , S. Malpezzi (Linear Fit) Supply elasticities are even more difficult to measure for commercial real estate Data Identifying restrictions: we don t know as much about demand for office space as we know about the demand for housing Still, a fertile field, given some time? A quick test: what can we infer about supply elasticities, if asset prices are rising? Flat? Falling? Commercial Real Estate Price Indexes based on NCREIF data National Council of Real Estate Investment Fiduciaries tracks institutional prices back to Problems with sample selection, and appraisal smoothing. Fisher, Geltner and Pollakowski, Journal of Real Estate Finance and Economics, 2007: Get NCREIF s raw data, base the index on transactions. Lose a lot of data, but the data you have is much better. Should reduce smoothing bias. Still subject to sample selection bias!!! Updated data available from MIT s website. Next slide splices the original index for early years with the more recent transaction index. Serious problems remain but the best we have over any long time frame

10 Real Commercial Real Estate Price Index Original NCREIF Capital Index ; MIT Transaction Index Supply elasticity of commercial real estate At least in the aggregate, until (say) 2005 or 2006, CRE prices were broadly consistent with fairly elastic supply. While scale/timing was different, CRE asset prices saw a boom and bust cycle post 2005 that was broadly parallel to housing s. (Housing prices peaked early in 2006, CRE prices peaked about a year later). Boom and bust in recent years: evidence of inelastic supply, or something else? Note: nobody s yet estimated supply elasticities of CRE directly, by market; or looked at determinants of same Back to the lab, again We need a lot more work on supply elasticities By market By property type Understanding dynamics (the time path of adjustment) Understanding the role of expectations