1. Designer Buildings: An Evaluation of the Price Impacts of Signature Architects Franz Fuerst, Pat McAllister and Claudia Murray University of Reading Henley Business School School of Real Estate and Planning

2. Research Question Do offices designed by architects who have won the AIA Gold Medal and/or the Pritzker Prize command a rental premium? More specifically: Whether, ceteris paribus, commercial offices designed by signature architects achieve rental premiums compared to commercial offices designed by non- signature architects

3. DEFINITIONS: ARCHITECTURE AND FAME Awards Competitions. Publications. Buildings Focus? Size? Client?

4. DEFINITIONS: AWARDS PRITZKER PRIZE AIA GOLD MEDAL To honour a living architect whose built work demonstrates a combination of those qualities of talent, vision, and commitment, who has produced consistent and significant contributions to humanity and the built environment through the art of architecture. The Gold Medal is the highest honor that the American Institute of Architects can bestow on an individual. It is conferred by the national AIA Board of Directors in recognition of a significant body of work of lasting influence on the theory and practice of architecture.

5. TRANSMISSION OF PRIZE TO PRICE Better design? (Not considered) Cost ↑,Benefits ↑ Prize Image benefits to =occupiers =owners =developers Enhanced prestige, recognizability Higher productivity? Price

6. PREVIOUS EMPIRICAL RESEARCH Hough and Kratz (1983) Sample: Rents 139 National and/or Chicago designated landmarks in Chicago. Method: Hedonic regression Findings: Premium only for newer landmark offices, for older buildings the coefficient was negative in all model specifications but insignificant. Vandell and Lane (1989) Sample: Rents and vacancy rates of 55 Class A office buildings in Boston and Cambridge, Massachusetts. Method: Examined the relationship between aesthetic qualities and rents/vacancy rates. Findings: Buildings rated in the highest quintile in terms of aesthetics had rents that were 22% higher than buildings rated in the lowest quintile controlling for differences in location, number of stories etc. Gat (1998) Sample: 50 office buildings in Tel Aviv, rents, physical and location features. Method: Hedonic regression, rating of building and design quality by architects. Findings: Marginal contribution of quality of architecture variable of 5.4% - --highest of a non-location variable in the model.

7. RESEARCH METHODS AND DATA This study draws on CoStar's comprehensive national database We have identified 490 office buildings designed by signature architects. In order to identify a control sample against which to compare the sample of buildings designed by signature architects, we have drawn upon the CoStar database (10,500 properties with full set of variables in our sample). (1) Hedonic regression modeling enables researchers to isolate the relative contribution of an attribute (or a bundle of attributes) and estimate the price effects. (2) Logistic regression: can be used to identify a suitable peer group based on probability scores. (3) Manual selection from database. Requires close inspection of property details and available comparables. DATA: CoStar METHODOLOGY

8. METHODOLOGICAL ISSUES Isolating price premiums Age Quality Tenant quality Location Lease structures Size Specifications Given heterogeneity, key methodological problem is to control for differences between ODSAs and non-ODSAs. ODSA may lift rental rates in surrounding area. Spillover effects Sample selection Our definition of a signature architect is unavoidably arbitrary. Comparable buildings may be designed by high profile architects. Iconic buildings need to be considered – designed by signature architects of the 1920s and 1930s? Skill, brand or expense? Is an identified price difference due to superior design, iconic status or additional expenditure?

9. New York DISTRIBUTION OF ODSAs IN AMERICA Los Angeles Washington Chicago Boston San Francisco

11. BASIC DESCRIPTIVE STATS ODSA (median)Non-ODSA (median) Rent psf $26.7$18 Price psf $215$115 Size 362,77110,800 Storey 162 Age 2123 Plot Size 1.541.16 Occupancy Rate 93.779.8 N Variable

12. HEDONIC EQUATION Rental rates Age of building (year of construction or major refurbishment) Size (sq.ft. of rentable space) Height (no. of stories) Size (sq.ft.) Lease type Location (submarket cluster) Building quality

13. HEDONIC REGRESSION RESULTS - RENTS CoefficientStd. Errort-StatProb. C2.940.1029.400.00 ODSA0.070.023.390.00 Age-0.040.00-17.670.00 LOG(RBA)-0.020.00-3.580.00 LOG(STORIES)0.050.01`8.920.00 NET LEASE-0.090.01-15.200.00 CLASS A0.220.0119.850.00 CLASS B0.080.0113.300.00 OCCUPANCY RATE0.00 3.280.00 Submarket cluster dummy variables included Adj. R-sq.: 0.61 F-test: 27.09 Prob. (F) 0.000

14. REGRESSION RESULTS – MID-TOWN RENTS CoefficientStd. Errort-StatProb. C3,431.093.150.00 ODSA0.090.081.100.27 AGE-0.110.06-1.790.08 LOG(RBA)0.000.060.050.96 LOG(STORIES)0.020.11`0.170.87 OCCUPANCY RATE0.180.200.910.37 Submarket cluster dummy variables included N=80 Adj. R-sq.: 0.32 F-test: 2.14 Prob. (F) 0.02

15. HEDONIC REGRESSION RESULTS - PRICES CoefficientStd. Errort-StatProb. C5.770.5510.510.00 ODSA0.270.064.720.00 AGE (VARIOUS BANDS – COEFFICIENTS ALL POSITIVE) LOG(RBA)-0.220.01-16.070.00 LOG(STORIES)0.160.02`9.240.00 NET LEASE-0.090.01-15.200.00 CLASS A0.490.0314.780.00 CLASS B0.060.022.960.00 TIME TREND0.030.0016.070.00 Submarket cluster dummy variables included Adj. R-sq.: 0.46 F-test: 8.70 Prob. (F) 0.000

16. Logit function Probability of ODSA x Logistic regression: Maximum likelihood function, probability (P) constrained between 0 and 1

17. Results of Logistic Regression Observed Predicted signature Percentage Correct 01 Step 1 signature 0 11,0158699.2 1 17620153.3 Overall Percentage 97.7 a. The cut value is.500

18. Results of Logistic Regression

19. Matched peer group characteristics

20. RESULTS USING LOGIT GENERATED SAMPLE ODSA sample RentSize (sq.ft.)StoriesYear builtYear renovated Land area % leased N276479478472109480 Mean 33.67 543,07122.42198019936.5888.79 Median 26.65 378,53816.00198419951.5593.70 Matched sample (p>0.13) RentSize (sq.ft.)StoriesYear builtYear renovated Land area% leased N277408 123 408 Mean 34.7 502,19119.7619821996 4.7688.42 Median 27.94 315,74916.0019861998 1.8195.72

21. CONCLUSIONS/ FURTHER WORK Hedonic analysis suggests a rent premium but peer group analysis based on results of logit model is inconclusive (raw averages vs. probability weighted) Model sales transaction prices Obtaining close comparables by manually selecting suitable properties from the CoStar database for each ODSA (case studies of NYC, LA and Chicago) More in-depth study of specific contribution of signature architects required

22. CONCLUSIONS/ FURTHER WORK More comprehensive assessment of overall added value of signature architecture: Neighbourhood externalities, quality of interior space etc.