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

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Presentation on theme: "Commercial real estate and nonlocal investors: price disparities on entry and exit Yu Liu Georgia State University Paul Gallimore University of Reading."— Presentation transcript:

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 o Turnbull and Sirmans (1993) o Watkins (1998) o Lambson, McQueen and Slade (2004) o Clauretie and Thistle (2007) o Ihlanfeldt and Mayock (2012)

3 Motivation for this study Prior ResearchThis Study Residential (single and multifamily) Office (CoStar COMPs® database) Single market138 markets Buying onlyBuying and Selling Smaller sample size (generally) 10,971 in purchase sample 11,444 in sales sample Varied sample horizons1996 through 2012 No sale conditions36 individual sale conditions & combinations No investor clienteles24 investor types No control for selection biasPropensity-score matching Nonlocal investors: 22% of purchase sample, 29% of sale sample 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 + β N ·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{β 0 + β X X + β T T + β Y Y + β C C + β M M} Use variable coefficients to produce estimate of probability that transaction involves nonlocal buyer Use this to match each nonlocal transaction with closest local transaction

8 Sales Local transactions: some post-match summary stats 2,283 169 117,415 78,067 3,335 142 112,995 64,486 Purchases

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

10 Results: Estimated premium/discount – nonlocal buyers Base case price effects Overpay by 13.8% Sell at discount of 7% 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 + β N ·I{Nonlocal}+ β S ·Distance + β R ·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 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 Sell at discount of 4.6% Discounting increases with distance Unaffected by nonlocal rent differential Sell at discount of 4.6% Discounting increases with distance Unaffected by nonlocal rent differential Nonlocal seller gets 1% less than locals for every 250 miles away from market

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 + β N ·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 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 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 As compared to local investors, nonlocal investors........


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