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Commercial real estate and nonlocal investors: price disparities on entry and exit

Commercial real estate and nonlocal investors: price disparities on entry and exit. Yu Liu Georgia State University Paul Gallimore University of Reading Jonathan A. Wiley Georgia State University. Primary question.

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Commercial real estate and nonlocal investors: price disparities on entry and exit

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  1. Commercial real estate and nonlocal investors: price disparities on entry and exit Yu Liu Georgia State University Paul Gallimore University of Reading Jonathan A. Wiley Georgia State University

  2. Primary question • Do nonlocal investors pay more than local investors for the same real estate assets? Previous work • Turnbull and Sirmans (1993) • Watkins (1998) • Lambson, McQueen and Slade (2004) • Clauretie and Thistle (2007) • Ihlanfeldt and Mayock (2012)

  3. Motivation for this study Nonlocal investors: 22% of purchase sample, 29% of sale sample

  4. Summary Statistics – Purchase Sample

  5. Summary Statistics – Sales Sample

  6. Method OLS Regression Model ln(Price/SF) = Controls + βN·I{Nonlocal} + ε Controls: Property characteristics, investor types, calendar year, sale conditions, geographic markets Expectation Purchases ( + ) Sales ( – )

  7. Propensity-score matching approach price paid by nonlocals vs. price paid by local buyers for exact same properties price paid by nonlocals vs. price paid by local buyers for similar properties   • Exclude local transactions that look least like nonlocal transactions • Perform whole sample probit regression with binary dependent variable Nonlocal (independent variables same as main model) Pr{Nonlocal = 1} = Φ{β0 + βXX + βTT + βYY + βCC + βMM} • Use variable coefficients to produce estimate of probability that transaction involves nonlocal buyerUse this to match each nonlocal transaction with closest local transaction

  8. Local transactions:some post-match summary stats Purchases 2,283 169 117,415 78,067 Sales 3,335 142 112,99564,486

  9. Results: Estimated premium – nonlocal buyers ln(Price/SF) = Controls + βN·I{Nonlocal} + ε

  10. Results:Estimated premium/discount – nonlocal buyers • Base case price effects • Overpay by 13.8% • Sell at discount of 7%

  11. What explains price differences? • Information Asymmetry – nonlocal investors less well-informed so get poorer deal when they both buy and sell Proxy: Distance • Market Anchoring – investors from higher value markets anchor valuations on those markets Means they overbid when they buy but they have to take the market price when they sell (unless they sell to another investor from a high-price market) Proxy: Rent difference

  12. What explains price differences? • Information Asymmetry – nonlocal investors less well-informed so get poorer deal when they both buy and sell Proxy: Distance • Market Anchoring –investors from higher value markets anchor valuations on those markets. Means they overbid when they buy but they have to take the market price when they sell (unless they sell to another investor from a high-price market) Proxy: Rent difference ln(Price/SF) =Controls + βN·I{Nonlocal}+ βS·Distance + βR·Rent diff + ε. Purchase sample: ( + ) ( + ) ( + ) Sales sample: ( – ) ( – ) ( 0 )

  13. Results:Information asymmetry and anchoring effects • Overpay by 9.1% • Overpayment increases with distance • Overpayment increases with rent differential e.g. Buyer located 600 miles away pays 600x0.007% = 4.2% more e.g. Buyer from market with rents 17.5% higher pays 17.5x7.6% = 1.3% more

  14. Results:Information asymmetry and anchoring effects Nonlocal seller gets 1% less than locals for every 250 miles away from market • Sell at discount of 4.6% • Discounting increases with distance • Unaffected by nonlocal rent differential

  15. Information Asymmetry:Additional test • Distance may be less than perfect proxy for information asymmetry • If nonlocal investors informationally disadvantaged, prices in transactions between them should: • reflect smaller premiums than when they buy from locals • reflect smaller discounts than when they sell to locals • Test this.......

  16. Information Asymmetry:Additional test • Estimate “between nonlocals” effect, using first model ln(Price/SF) = Controls + βN·I{Nonlocal} + ε • ......apply to pooled sub-sample (produced by propensity score matching ) 657 “between nonlocals” transactions matched with most similar “between locals” transactions Now describes transaction type rather than investor

  17. Results:Nonlocal/Nonlocal vs. Local/Local transactions Overvalue by 6.3% when nonlocals buy from nonlocals(sell to nonlocals) Overpayment much smaller than when nonlocals buy from locals (13.8%) Discount, accepted when nonlocals sell to locals (7%), disappears

  18. Findings As compared to local investors, nonlocal investors........ • Overpay on purchase by estimated 13.8%. • Discount on sale by 7% • Overpayment positively related to distance (information asymmetry) and rent differentials (anchoring) • Discounting also increases with distance (information asymmetry) • Pay smaller premiums when buying from other nonlocals and no discount when selling to other nonlocals

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