Commercial real estate and nonlocal investors: price dispar - PowerPoint Presentation

Slide1

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 UniversitySlide2

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) Slide3

Motivation for this study

Prior Research

This Study

Residential (single and multifamily)

Office (CoStar COMPs® database)Single market

138 markets

Buying

onlyBuying and SellingSmaller sample size (generally)10,971 in purchase sample11,444 in sales sampleVaried sample horizons1996 through 2012No sale conditions36 individual sale conditions & combinations No investor clienteles24 investor typesNo control for selection biasPropensity-score matching

Nonlocal investors:

22

% of

purchase sample,

29% of

sale sampleSlide4

Summary Statistics –

Purchase SampleSlide5

Summary Statistics –

Sales SampleSlide6

Method

OLS Regression Model

ln

(Price/SF) = Controls +

βN·I{Nonlocal} + εControls: Property characteristics, investor types, calendar year, sale conditions, geographic markets

Expectation

Purchases ( + )Sales ( – )Slide7

Propensity-score matching approach

price paid by

nonlocals

vs. price paid by local buyers for

exact same propertiesprice 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

+

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 transactionSlide8

Sales

Local transactions:

some post-match summary stats

2,283

169

117,415

78,067

3,335

142

112,99564,486

PurchasesSlide9

Results:

Estimated premium – nonlocal buyers

ln

(Price/SF) = Controls +

β

N

·

I{Nonlocal} + εSlide10

Results:

Estimated premium/discount – nonlocal buyers

Base case price effects

Overpay

by

13.8

%

Sell at discount of 7%Slide11

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 differenceSlide12

What explains price differences?

Information Asymmetry

– nonlocal investors less well-informed

so get poorer deal when they both buy and sell

Proxy: DistanceMarket 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 )Slide13

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% moree.g. Buyer from market with rents 17.5% higher pays 17.5x7.6% = 1.3% moreSlide14

Results:

Information asymmetry and anchoring effects

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 marketSlide15

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 localsreflect smaller discounts than when they sell to localsTest this.......Slide16

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” transactionsNow describes transaction type rather than investorSlide17

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%), disappearsSlide18

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 nonlocalsAs compared to local investors, nonlocal investors........