Methodology and Hypothesis Formation

Table 2.1 provides descriptive statistics about the quarterly NPI total returns (NPI), quarterly NPI capital-appreciation returns (NPI_CR), absolute and weighted Positive- Negative-Ratios (PNR_A and PNR_W) and our macroeconomic control variables. For each variable, we report the mean, median, standard deviation (SD), minimum (Min) and maximum (Max). The average quarterly total returns of the private CRE during our sample period is 2.19% and ranges between -8.40% and 5.49%, given the high volatility during the boom and bust phases that are part of our sample period. Capital- appreciation returns are associated with lower quarterly values, ranging between -9.66% and 3.89% with a mean (median) of 0.65% (1.27%). The average PNR_W value (7.60) is more than three times of the PNR_A value (2.27), which depicts the importance of distinguishing between the two measures and sheds light on the strength of the respective sentiment. The average quarterly INFLATION during the sample period was 0.52%, while TERM and SPREAD float around 1.1% and 2.1%, on average. Table 2.1: Descriptive Statistics

Statistic Mean Median SD Min Max

NPI (%) 2.191 2.687 2.550 -8.399 5.490 NPI_CR (%) 0.648 1.268 2.545 -9.655 3.889 PNR_A 2.272 1.592 1.142 0.912 4.814 PNR_W 7.601 5.619 4.453 2.057 17.530 INFLATION (%) 0.518 0.584 1.021 -3.910 2.476 TERM (%) 2.103 2.240 1.021 -0.380 3.580 SPREAD (%) 1.104 0.975 0.453 0.550 3.380

Notes: Table 2.1 reports summary statistics of variables used in the analysis on a quarterly basis. NPI is

the total return of the NPI and NPI_CR is the capital appreciation return. PNR_A and PNR_W are the absolute and weighted Positive-Negative-Ratio sentiment measures, respectively. INFLATION is the percentage change of the Consumer Price Index (CPI). TERM is the spread between the ten-year US Treasury Bond and the 3-Month Treasury Bill yields. SPREAD is the spread between Baa- and Aaa- rated corporate bonds yields. The sample period is 2001:Q1 to 2016:Q4.

2.5 Methodology and Hypothesis Formation

2.5.1 Visual and Correlation Analysis

As our preliminary visual analysis, we plot the media-expressed sentiment measures against the returns of the private CRE market. Specifically, we plot the deviation of

2.5 Methodology and Hypothesis Formation the sentiment measure from its 1-year moving average relative to the quarterly CRE total returns. This type of plot would illustrate the general relationship between changes in market sentiment and CRE returns and highlights whether market sentiment leads or lags returns.Additionally, we calculate the respective correlations between our quarterly sentiment values and CRE quarterly returns.

2.5.2 Regression Analysis

We begin our empirical analysis by investigating the ability of real-estate related sentiment, expressed in the news, to predict total returns on the private CRE market in the US. To do so, we regress the NPI total return on the lagged absolute or weighted Positive-Negative-Ratios. By regressing CRE returns on our lagged media-expressed sentiment values, we test the hypothesis that market sentiment predicts future returns of the private CRE market.

Hypothesis 1: Real estate market sentiment predicts future returns of the private CRE market.

In addition to lagged media-expressed real estate sentiment, the regression specifications also control for other relevant macroeconomic variables proven to affect CRE market returns, (see e.g. Clayton et al., 2009 and Ling et al., 2014). Controlling for the term structure of interest rates is relevant because it is related to commercial real estate financing cost and expectations of future economic developments. Accounting for the percentage changes in the Consumer Price Index (CPI) is important because many commercial rental contracts are linked to inflation and therefore affect future returns. The spread between Baa- and Aaa-rated corporate bonds yields reflects the overall business conditions and general default risk in the economy. Finally, we include a dummy variable to control for any factors associated with the global financial crisis (GFC) from 2007:Q3 to 2008:Q4. Autocorrelation and heteroscedasticity issues are accounted for by using Newey and West (1987) robust standard errors.

2.5 Methodology and Hypothesis Formation Formally, we estimate the following equation:

∆𝑁𝑃𝐼_𝑡 = 𝑐 + ∑ ∝_𝑖 (∆𝑃𝑁𝑅_𝑡−𝑖) 𝑖=5 𝑖=1 + 𝛽₁(∆𝐼𝑁𝐹𝐿_𝑡) + 𝛽₂(∆𝑇𝐸𝑅𝑀_𝑡) + 𝛽₃(∆𝑆𝑃𝑅𝐸𝐴𝐷_𝑡) + 𝐺𝐹𝐶_𝑡+ 𝜀_𝑡, (2.2)

where 𝑁𝑃𝐼𝑡 is the total return during quarter 𝑡; 𝑃𝑁𝑅𝑡−𝑖 is the Positive-Negative-Ratio

to measure media-expressed sentiment with 𝑖 quarterly lags; 𝐼𝑁𝐹𝐿_𝑡 is the inflation rate, 𝑇𝐸𝑅𝑀𝑡 the interest term ensure structure and 𝑆𝑃𝑅𝐸𝐴𝐷𝑡 the spread between Baa- and

Aaa-rated corporate bonds. 𝐺𝐹𝐶 is a dummy variable to indicate the global financial crisis and 𝜀_𝑡 represents the error term. Except of the crisis dummy, all variables are applied in first differences to stationarity.6

2.5.3 Vector Autoregressive Analysis

The multiple linear regression model described above estimates the value of the dependent variable (NPI) using several, supposedly independent, variables. However, it could be presumed that our media-expressed sentiment measures also contain information about past CRE market performance as indicated by the proposed News- Impact-Model of Section 2.4.2. Consequently, we examine the bi-directional relationship between media-expressed sentiment and the performance of the private US CRE market using a Vector Autoregressive (VAR) framework. According to this model, each variable is a linear function of lags of itself and lags of other variables. Hence, the VAR model allows us to estimate the intertemporal links between media- expressed sentiment and the private CRE market and address the potential endogeneity problem. Furthermore, the VAR model enables us to analyze whether the media- expressed sentiment predicts returns on private CRE, even when controlling for the lags of the NPI itself, which is shown to contain momentum (Beracha and Downs, 2015). Formally, the VAR model used in our analysis is specified as the following:

6 _{For results of the augmented Dickey-Fuller tests for the presence of unit roots, i.e. non-stationarity}

2.5 Methodology and Hypothesis Formation ∆𝑁𝑃𝐼_𝑡 = 𝛼₁₀+ ∑ 𝛽_1𝑖(∆𝑁𝑃𝐼_𝑡−𝑖) 𝑖=5 𝑖=1 + ∑ 𝛾_1𝑖(∆𝑃𝑁𝑅_𝑡−𝑖) 𝑖=5 𝑖=1 + 𝛿1(∆𝐸𝑥𝑜𝑔𝑡) + 𝜀1𝑡 ∆𝑃𝑁𝑅𝑡 = 𝛼20+ ∑ 𝛽2𝑖(∆𝑃𝑁𝑅𝑡−𝑖) 𝑖=5 𝑖=1 + ∑ 𝛾2𝑖(∆𝑁𝑃𝐼𝑡−𝑖) 𝑖=5 𝑖=1 +𝛿₂(∆𝐸𝑥𝑜𝑔𝑡) + 𝜀2𝑡. (2.3)

The variables are as described above and defined in equation (2.2). Note that, for brevity, the control variables (𝐼𝑁𝐹𝐿_𝑡, 𝑇𝐸𝑅𝑀_𝑡 and 𝑆𝑃𝑅𝐸𝐴𝐷_𝑡) are summarized in 𝐸𝑥𝑜𝑔𝑡7. 𝜀1𝑡 and 𝜀2𝑡 are the error terms. The endogenous variables are quarterly NPI

returns (𝑁𝑃𝐼_𝑡−𝑖) and the media-expressed sentiment (PNR_A or PNR_W). We include lags up to t-5 based on the Akaike Information Criteria (AIC) for various choices of the lag length p. Applying the Augmented-Dickey-Fuller unit root test (see Dickey and Fuller, 1979; Said and Dickey, 1984) suggests using first differences of all variables to ensure stationarity.

2.5.4 Granger Causality Tests

We further examine the bi-directional relationship between media-expressed sentiment and CRE returns, by conducting pairwise Granger causality tests (Granger, 1969). This type of analysis helps us better understand the lead-lag relationships between sentiment in real estate related news and the private CRE market. We hypothesize that media- expressed sentiment drives total returns of the private CRE market, but not the other way around. We base our hypothesis on evidence from the literature that the CRE market is not fully efficient and is slow to react to new market information. Formally, our hypothesis is stated as the following:

Hypothesis 2: Media-expressed sentiment predicts future returns of private commercial real estate, but returns on private commercial real estate do not predict future media-expressed sentiment.

7 _{Note that when the crisis dummy is included, results are similar with respect to the sign, size and the}