1. Does Property Transactions Matter in Price Discovery in Real Estate Market: Evidence from the US firm level data William Cheung and James Lei University of Macau, Macau China ERES 2014 Bucharest University of Economics

2. Motivations The results of public market (REITs stock) V.S. private real estate market are mixed. - Hoesli et al. (2013) find that public real estate market leads the private real estate market. - Yavas et al. (2011) find that there are variations across firms within each property type. For any given property category, REIT returns could be leading NAV returns for some firms while NAV returns could be leading REIT returns for some other firms. - Tuluca et al. (2000) find that private market seems to informationally lead the public one. Ross (1987) defined a market as efficient if there is a lack of arbitrage opportunity. Therefore, private real estate market makes itself as a compelling case for efficiency because of illiquidity. Duffie, Malamud, and Manso (2010) find that private information sharing promotes the effect of public information sharing.

3. Main Findings Significant contributions to price discovery from the private markets. Price discovery from the private markets increase further relative to the public real estate market, when employing transaction windows, as compared to full samples. Impulse response analysis shows that private real estate market converges even faster than public market real estate market around transaction windows. The results are robust to length of transaction windows and property types.

4. Our Uniqueness A unique dataset of daily property transactions covering 01/02/2001 to 12/31/2013. Synchronized public and private pairs around transaction windows, not by regular calendar days as in the earlier studies in the literature. Estimate long-run relation between public and private real estate markets with respect to information generated by property transactions in the underlying spot market. Unique environment of property markets and transaction data allow us to provide empirical evidences on private and public information sharing.

5. Contributions We provide a new angle to test the relative contributions to price discovery between public and private real estate markets: the comparison between full samples and transaction windows. Transaction windows matter because either the appraisal-based or transaction-based values of the underlying properties should react to the new information of property transactions and incorporate it into new values. Though public real estate market dominates the price discovery with respect to private real estate market, as stated in literatures, we fill the gap that private real estate market can become informative when transaction windows are taken into account.

6. Data Source: SNL financial Full samples: from 02 January 2001 to 31 December 2013 Full samples: Property type DiversifiedOfficeHotelIndustrial Number of firms 2419268 Sample Size 106,80169,66146,76221,245 Number of Transactions 1,1161,316717 633

7. Data Transaction windows: –lead_lag 25 days, based on each transaction date t, we include [ t-25, t+25 ] observations Example 1: there was a property transaction on 04/25/2013 of Starwood Hotels & Resorts Worldwide. To construct the transaction window of lead_lag 25 days, based on 04/25/2013, we include the observations of [ t-25, t+25 ]. Therefore, the transaction window will be from 03/20/2013 to 05/30/2013, only weekdays included. –lead_lag 30 days, based on each transaction date t, we include [ t-30, t+30 ] observations –transaction_date_lag 5 days & lead_lag 25 days, based on each transaction date t, we set t-7 as each new transaction date, denoted as t 7, and include [t 7 -25, t 7 +25] observations Example 2: there was a property transaction on 04/25/2013 of Starwood Hotels & Resorts Worldwide. To construct the transaction window of transaction_date_lag 5 days & lead_lag 25 days, we first lag the transaction date back to 5 days which should be 04/18/2013. Then, based on 04/18/2013, we include the observations of [t 7 -25, t 7 +25]. Therefore, the transaction window will be from 03/13/2013 to 05/23/2013, only weekdays included.

8. Data Property type DiversifiedOfficeHotelIndustrial Number of firms 2418218 Sample Size 26,16232,72721,17911,210 Number of Transactions 1,1161,316717 633 Lead_lag 25 days Lead_lag 30 days Property type DiversifiedOfficeHotelIndustrial Number of firms 2418218 Sample Size 28,19935,207 23,321 11,935 Number of Transactions 1,1161,316717 633 Property type DiversifiedOfficeHotelIndustrial Number of firms 2418218 Sample Size 26,17032,717 21,202 11,188 Number of Transactions 1,1161,316717 633 Transaction_date_lag 5 days & lead_lag 25 days

9. Methodology Vector Error Correction Model (VECM) where  Total_return and  NAV are the change of total return index and net asset value (NAV) in period t, respectively, Z = Total_return b NAV is the long-term relationship between total return index and NAV, and are i.i.d. innovations.

10. Methodology Gonzalo and Granger ratios (common factor loadings) Gonzalo and Granger's (1995) price discovery focus on the error correction process. The model estimates the common factor weights that reflect the permanent contribution to the common factor (efficient price). The common factor weights are derived from each market's error correction coefficients. Superior price discovery is attributed to the market with the higher GG ratio.

11. Tables of GG Ratios Property type Full sampleLead_lag_25_days Total_retur n NAV Total_retur n NAV Diversified29%71%18%82% Office16%84%11%89% Hotel28%72%19%81% Industrial39%61%6%94% GG ratios between full samples and lead_lag 25 days

12. Tables of GG Ratios Property type Full sampleLead_lag_30_days Total_retur n NAV Total_retur n NAV Diversified29%71%7%93% Office16%84%25%75% Hotel28%72%23%77% Industrial39%61%18%82% GG ratios between full samples and lead_lag 30 days

13. Tables of GG Ratios Property type Full sample Transaction_date_lag_5 days & Lead_lag_25 days Total_retur n NAV Total_retur n NAV Diversified29%71%20%80% Office16%84%11.5%88.5% Hotel28%72%22%78% Industrial39%61%28%72% GG ratios between full samples and transaction_date_lag 5 days & lead_lag 25 days

14. The GG ratios (common factor loadings) of private real estate market increase further relative to public real estate market, when considering transaction windows.

15. Graphs of Impulse Response of NAV for Starwood Hotels & Resorts Worldwide

16. The reaction of NAV to shocks of the three transaction windows converges faster than that to shocks of full samples The slopes of the dashed lines are steeper than those of solid lines The distance between two dashed lines becomes narrower than solid lines

17. Conclusions Consistent with Oikarinen et al. (2011), Hoesli et al. (2012), we find that public and private real estate market exhibit long-term cointegrating relationship We also find that public and private real estate market exhibit long-term cointegrating relationship with samples of transaction windows We test the relative contributions to price discovery between public and private real estate markets around transaction windows and find that the information content in the real estate market increases further, as compared with that of full samples. Private real estate market does matter in price discovery around transaction windows

18. Robustness – Normalized Co- integrating Vector Property type Full samples Lead_lag 25 days Lead_lag 30 days Transaction_da te_lag_7 days & lead_lag 25 days Diversified0.1430.130 Office0.1110.091 0.096 Hotel0.1360.130 Industrial0.127 Comparison of the normalized cointegrating vector between full samples and transaction windows. The normalized cointegrating vector of transaction windows show the robustness.

19. Robustness – Impulse Response of One Liberty Property Inc

20. Robustness – Impulse Response of Forest City Enterprises Inc

21. Robustness – Impulse Response of Kilroy Realty Corporation

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