Interest Rates and Fundamental Fluctuations in Home Values Albert - - PowerPoint PPT Presentation

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Interest Rates and Fundamental Fluctuations in Home Values Albert - - PowerPoint PPT Presentation

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Saiz Interest Rates and Fundamentals Interest Rates and Fundamental Fluctuations in Home Values Albert Saiz 1 Saiz Interest Rates and Fundamentals Focus Changes in the user cost of capital driven by lower interest/mortgage rates and

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Interest Rates and Fundamental Fluctuations in Home Values

Albert Saiz

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Focus Changes in the user cost of capital driven by lower interest/mortgage rates and financial innovations (risk premium

  • n real estate investments)

Without a doubt, these have been the main factor used to explain evolution of housing prices during the boom

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Real Estate Valuation

Gordon valuation model (Poterba, 1984) Cap rate method (income comparables) Comparables

r V i g  

 r V c

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Agenda Combine the urban economics model (Alonso-Muth-Mills) with the asset pricing (Poterba, 1984) approaches Study impact of changes in user cost of capital Examine the roles of:

  • Replacement costs
  • Housing/land Supply elasticity
  • Demand for space
  • Rent endogeneity
  • Heterogeneity in rents

Parsimony

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Saiz: Interest Rates & Fundamentals in Housing 90 100 110 120 130 140 150 199901 199903 200001 200003 200101 200103 200201 200203 200301 200303 200401 200403 200501 200503 200601 200603 200701 200703 200801 200803 0.00 1.00 2.00 3.00 4.00 5.00 6.00 Price Index REAL Mortgage rate

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90 100 110 120 130 140 150 199901 199903 200001 200003 200101 200103 200201 200203 200301 200303 200401 200403 200501 200503 200601 200603 200701 200703 200801 200803 Price Index Explainable with CC

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Saiz: Interest Rates & Fundamentals in Housing 90 110 130 150 170 190 199901 199903 200001 200003 200101 200103 200201 200203 200301 200303 200401 200403 200501 200503 200601 200603 200701 200703 200801 200803 New England Middle Atlantic Pacific Explainable with CC

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Beverly Hills (Zip Code 90210): from Matt Kahn

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http://mysite.verizon.net/vodkajim/housingbubble

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http://mysite.verizon.net/vodkajim/housingbubble

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http://mysite.verizon.net/vodkajim/housingbubble

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http://mysite.verizon.net/vodkajim/housingbubble

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90 100 110 120 130 140 150 199901 199903 200001 200003 200101 200103 200201 200203 200301 200303 200401 200403 200501 200503 200601 200603 200701 200703 200801 200803 East South Central West South Central West North Central East North Central Explainable with CC

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http://mysite.verizon.net/vodkajim/housingbubble

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http://mysite.verizon.net/vodkajim/housingbubble

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Housing Supply and Pricing

The Model

Most parsimonious AMM that captures main issues

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Owner-renter assumption In this paper I conceptually and conventionally assume everyone is a renter Arbitrage between buyers: landlords versus owner-occupiers Can think of owners as leasing property to themselves

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Consumer Utility

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Rental Price of a Housing Unit

a non-arbitrage condition defines the rent profile with respect to distance

all city inhabitants attain utility Uk

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The Fundamental Rental Arbitrage Condition Leasing cost of structure Ricardian markup

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Graphic: rents and prices per square foot Prices Rents /sq.ft. r(d) v*cc Φ

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Growth

Utility at border Utility at exurbs:

And posit:

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Radius and Population: Inelastic Land Supply

Λ

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Radius and Population: Elastic Land Supply

Λ

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Housing Asset Pricing Equation: Theory At any point in time, asset market arbitrage for home-owner (can think of this as natural tendency) Main arbitrage formula in housing markets (Poterba, 1984)

  • τ stands for time (e.g. 2015)
  • d stands for distance to employment/amenities
  • With being the user cost of the

capital invested (frozen) into housing

  • The formula simply reads: the annual cost to the owner has to

be equal to the annual benefit (capital appreciation plus rent) At any point in time, asset market arbitrage for home-owner (can think of this as natural tendency) Main arbitrage formula in housing markets (Poterba, 1984)

  • τ stands for time (e.g. 2015)
  • d stands for distance to employment/amenities
  • With being the user cost of the

capital invested (frozen) into housing

  • The formula simply reads: the annual cost to the owner has to

be equal to the annual benefit (capital appreciation plus rent)

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  • The previous differential equation can be solved if we have an

expected rental-equivalent growth “temporal path” for every period and distance : r(τ,d)

  • In fact, the path of rental (use value) growth is quite

predictable in most markets

  • As discussed in economics part of the class, rental growth

quite predictable by economic, demographic, and consumption trends

  • These trends are quite persistent
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Graphic: rents and prices per square foot Prices Rents /sq.ft. r(90) v*cc Φ90 r(2010) Φ2010

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Vancouver, Canada

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Capitalization of Rents

An exponential growth rate can be rationalized for 1 ‘representative’ location:

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Graphic: rents and prices per square foot Prices Rents /sq.ft. r(d) v*cc cc p(d) Φ

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Changes in User Cost in the AMM Model

  • In the short to medium run I will assume that population is

not mobile. Note that there are four main effects of changes in v:

  • Decrease in structural user rents
  • Increase in demand for space and larger homes: land rents

increase everywhere

  • Structure-intensive locations more attractive: land rents

decrease in central locations

  • Discounting effect in price equation: applicable only to land
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Other Manufactured Durables

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Computer Price Index, consumer, monthly (index, 2001=100) Other Manufactured Durables Leasing cost with decreasing interest rates?

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Other Manufactured Durables

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Consumer Price Index: New Cars Leasing Costs?

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Manufactured Homes

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Evolution of Home Prices: Manufactured Single

Real Price of Single Manufactured Home 26000 27000 28000 29000 30000 31000 32000 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Real Price of Single Manufactured Home

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Financing/leasing a mobile home Buy a 30,000 mobile structure: Mortgage rate 8%: Annual payment 2,641 Mortgage Rate 6% Annual Payment 2,158 20% percent cheaper (v*cc)

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Graph A: rents and prices per square foot Rents /sq.ft. r1(d) v1*cc Φ

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Graph B: rents and prices per square foot: arbitrage Rents /sq.ft. r1(d) v1*cc v2*cc r2(d) Φ

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Graph C: rents and prices per square foot: land is complementary to structural demand Rents /sq.ft. r1(d) v1*cc v2*cc r2(d) r3(d) Φ2 Φ

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Graph D: compensate central locations for low structural intensity Rents /sq.ft. v2*cc r2(d) r3(d) r4(d) Φ2

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Notional Rents are Endogenous to User Costs Structural Share on Rents (versus land) Relative Size at Edge Demand Supply of Space (Land)

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Housing Values and Changes in User Cost

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Main takeaways

  • Rents should decline with lower user costs: proportionally more in areas where

land is not valuable

  • Demand for space goes up
  • Land rents go up generally… but less in land-intensive locations
  • Rental payments may go down, but less so in elastic supply areas
  • Land values should go up unambiguously, but structure values should not change!
  • Final increase in home values depends on:
  • Land shares (discounting effect + increase in land rents )
  • Supply elasticity (impact of increased demand on land rental payments)
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Housing Supply and Pricing

Empirics: Boom, Elasticity and Land Shares

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Price Growth During the Boom and Supply Elasticity

20 40 60 80 100 120 .5 1 1.5 2 Inverse of Supply Elasticity Price Growth 2000-2005 (1st Quarter) Fitted values

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Price Growth During the Boom and Land Shares

20 40 60 80 100 120 .2 .4 .6 .8 Land Share in 1990 Price Growth 2000-2005 (1st Quarter) Fitted values

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Inverse of Supply Elasticity 31.445 26.013 32.138 22.349 (6.605)*** (5.549)*** (6.556)*** (6.418)*** Land Share in 1990 85.802 99.675 104.469 65.224 (14.574)*** (14.654)*** (17.572)*** (15.150)*** Log Price 2000 - Log Price 1970

  • 18.631

(9.993)* Middle Atlantic

  • 1.535

(20.037) East North Central

  • 10.304

(20.207) West North Central

  • 5.673

(20.979) South Atlantic 2.53 (19.732) East South Central

  • 17.299

(20.722) West South Central

  • 17.963

(20.631) Mountain

  • 14.237

(20.117) Pacific 14.931 (19.594) Observations 137 137 137 137 R-squared 0.52 0.6 0.53 0.62 Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Price Growth 2000-2005 (1st Quarter)

Real Estate Boom and Fundamentals

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Housing Supply and Pricing

“Expected” Growth and Bust

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“Expected” Growth and Subsequent Bust

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20 40 20 40 60 80 100 Predicted 2000-2005 Price Growth Using Supply Elasticities

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“Expected” Growth and Subsequent Bust

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20 40 20 40 60 80 100 Predicted 2000-2005 Price Growth Using Supply Elasticities Price Growth 2005:Q1 - 2008:Q2 Fitted values

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Housing Supply and Pricing

Unexplained Growth and Bust

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Unexplained Growth and Subsequent Bust

  • 20

20 40

  • 40
  • 20

20 40 60 Price Growth (2000-2005) Unexplained by Supply Differences

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Unexplained Growth and Subsequent Bust

  • 20

20 40

  • 40
  • 20

20 40 60 Price Growth (2000-2005) Unexplained by Supply Differences

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Conclusions Under the null of common shocks to the user cost of housing capital:

  • Rents on structure should fall
  • Land rents should increase in most locations…
  • … but less so in land-intensive areas
  • Aggregate rents should decrease, specially with low land values
  • Rental payments should decrease with inelastic demand
  • Rents endogenous and contingent: P/R ratios not useful
  • Land Rental payments should increase more with inelastic supply
  • Land prices should increase: discounting + rental growth
  • Construction value should not change
  • Home values should go up more in areas with high land ratios

More or less what happened… deviations are being largely eliminated