Hedonic Geospatial Risk Model of the San Francisco Bay Area Apartment Market

or“All Locations Are Equal But Some Are More

Equal Than Others”

October, 2006

Richard B. GoldVice President Research and Investment StrategyGrosvenor Americas & Grosvenor Investment Management USA

Part OnePart One

BackgroundBackground

Why Worry?

• Investors should pay attention to both risk and return

• We are price takers, not price setters– Therefore are we getting enough return for the

risk we are taking?

• Deal-level “risk” more difficult to measure/predict than market-level risk– Large sample property performance data rare

• Not all properties are at the mean

Why Worry? (con’t)

• We do not buy the market/mean– Capital constrained– Private equity “lumpy”– No Bay Area apartment index to buy– Location is not only critical factor– Idiosyncratic hedonics equally critical

• Investors who can identify properties with higher/lower risk can:– Generate “alpha” – Sleep more peacefully

Part TwoPart Two

ModelModel

Objective

• Build a geo-spatial model with hedonic variables to help identify apartment cash flow risk factors

• Not all properties perform equally during cycles– Often location effect appears to dominate– Possible to filter out location and identify other

factors

• Ultimate goal– Provide acquirers with understanding of what

building characteristics affect cash flow volatility and ultimately asset pricing

What We Wanted to Avoid

• Objective was not to:– Build a “surgical”

forecasting/investment model– Signs and elasticities most important

Model & Structure

• Model employs following data:– Cash flow proxies for over 1400 apartment buildings

(Effective Rent * Occupancy)– Location: San Francisco Bay Area– Source REIS Reports– Period 1999 - 2005

• Risk measure (Dependent Variable):• Parkinson’s measure of extreme volatility:

[ln(High Revenue/Low Revenue )]2/4ln(2)

• We are looking for the volatility of a property’s intra-period cash flow

• “Period” is defined as the beginning of the dot.com/high tech bust

• Parkinson’s measure better capture intr-period volatility better than the traditional measure –standard deviation

Using Parkinson’s Measure

• Distribution of volatility for 1400 plus Bay Area apartment properties 2000-2005

Explanatory Variables

• Model employs following data:– Cash flow proxies for over 1400 apartment buildings

(Effective Rent * Occupancy)– Location: San Francisco Bay Area– Source REIS Reports– Period 1999 - 2005

• Risk measure (Dependent Variable):• Parkinson’s measure of extreme volatility:

[ln(High Revenue/Low Revenue )]2/4ln(2)

• We are looking for the volatility of a property’s intra-period cash flow

• “Period” is defined as the beginning of the dot.com/high tech bust

• Parkinson’s measure better capture intr-period volatility better than the traditional measure –standard deviation

Data: Property Locations

Distribution of Sample Properties by Year Built

Distribution of Sample Properties by Number of Units

Distribution of Sample Properties by Year Renovated

Limited and non-robust sample.Do description of “renovation.”

Hispanic Population by Location

African American Population by Location

Population 25 to 34 by Location

Household without Children by Location

African American Population by Location

Part ThreePart Three

ResultsResults

Distance Profiles

6701627Straight Line SJ

6801222Straight Line OAK

7101423Straight Line SF

8212033Drive Dist SJ

8011529Drive Dist OAK

8801830Drive Distance SF

12723252Drive Time SJ

12512446Drive Time OAK

13602747Drive Time SF

MaximumMinimumStandard DeviationMeanVariable

Regression Results

Number of obs 1440F( 9, 1430) 188.3Prob > F 0R-squared .54Adj R-squared .54Root MSE .07Variable Coef. Std. Err. t-stat P>|t|Drive time SF -0.00098 0.00041 -2.4 0.017 -0.0018 -0.0002Drive time OA 0.001183 0.00044 2.7 0.007 0.0003 0.0020Drive time SJ -0.00166 0.0001 -16.51 0 -0.0019 -0.0015SF household without child 0.049045 0.01394 3.52 0 0.0217 0.0764SJ household without child 0.057948 0.01331 4.35 0 0.0318 0.0841Log units 0.015192 0.0019 7.99 0 0.0115 0.0189EthnicityHispanic2000 -0.06862 0.01261 -5.44 0 -0.0934 -0.0439RaceBlack2000 -0.07654 0.02744 -2.79 0.005 -0.1304 -0.0227Age 2000_2534 0.122611 0.03072 3.99 0 0.0624 0.1829Constant 0.235377 0.01655 14.22 0 0.2029 0.2678

95% Confidence Interval

Interpretation of Results

• Location is important but not everything– San Jose most volatile– Best location, ceteris parabis, downtown

Oakland

• Other factors:– Demographics

• Cohorts– Younger households increase volatility

• Ethnicity– Black and Hispanic households reduce risk

• Household types– Households with children increase volatility

– Project size:• More units more volatility

Results (con’t)

• In addition to the calculating the global effects– Estimated the model with Geographically

Weighted Regression (GWR) (Fotheringham, et. al, 2000).

– Methodology allows estimating the local effect at any location in the geographical area

• Uses information from nearby observations weighted by their distance to the location of interest.

• Allows you to fill in the blanks in locations for specific locations for which you don’t have a property.

Part FourPart Four

ConclusionsConclusions

Conclusions

• Location is important but not everything• Virtually every apartment building in the

SF Bay Area was hurt but…– Not all apartments buildings were equally

effected even taking into consideration distance to market

• Larger properties and most likely “Class A” properties more vulnerable

• Families are more vested in their location• Minorities less foot loose• We can now set some basic parameters

for acquirers• Next Steps?

Next Steps

• Add more hedonic and geospatial variables:– Distance to public transportation nodes– Distance to schools/hospitals/other social

service locations

• Reexamine property hedonics:– Can age and renovation data be better

defined?

• Tighten sample geography– Eliminate properties more than ?? mins/miles

from employment centers

• Check for spatial autocorrelation