REAL ESTATE MARKETS
VARIABLE OBS MEAN ST DEV MIN MAX.
Minimum asking price per m2(€) 5,526 69.07 50.72 2.88 520.00
Maximum asking price per m2(€) 5,526 83.23 63.73 3.17 690.00
Visible from motorway (dummy) 5,526 0.22 0.42 0.00 1.00
Excellent motorway access (dummy) 5,526 0.22 0.42 0.00 1.00
Good motorway access (dummy) 5,526 0.25 0.44 0.00 1.00
Poor motorway access (dummy) 5,526 0.52 0.50 0.00 1.00
Heavy industry site (dummy) 5,526 0.06 0.24 0.00 1.00
Business park (dummy) 5,526 0.08 0.27 0.00 1.00
Warehousing and distribution site (dummy) 5,526 0.02 0.15 0.00 1.00
Mixed use site (dummy) 5,526 0.84 0.37 0.00 1.00
Land availability (share) 5,526 0.09 0.06 0.00 0.43
Competition intensity (share) 5,526 0.31 0.26 0.00 0.99
Core city (dummy) 5,526 0.18 0.38 0.00 1.00
We construct two variables that define the location of the industrial site in relation to the motorway network. First, a dummy variable is created that takes value 1 if the industrial site is intersected by a motorway (using a 100 metre buffer). The presence of a motorway in the immediate vicinity of a site implies that it will be visually prominent from the road. Second, we calculate the straight air line distance from each site to the nearest motorway junction. We use this information to categorize industrial sites in terms of access to the motorway network. Our classification is based on Geuting et al. (2011) and three dummy variables are constructed. The first variable indicates whether or not sites are located within the first 1,000 metres from a motorway junction (the ‘excellent’ access group). The second dummy variable indicates whether or not sites are located at a distance between 1,000 and 3,000 metres from the motorway junction (good access). The third variable identifies sites beyond 3,000 metres from the nearest motorway junction (poor access). Finally, data obtained from Netherlands Statistics (CBS) have been used to determine if an industrial site is located in a core city (referred to as a metropolitan area) or elsewhere in the Netherlands. The dataset is described in Table 3.1.
3 DETERMINANTS OF INDUSTRIAL LAND PRICES
3.6 DETERMINATION OF ASKING PRICES -
QUANTITATIVE EVIDENCE
T
his section presents the estimation results of the proposed model. The equation was estimated separately for minimum and maximum asking prices using ordinary least squares (OLS).40 Table 3.2 reportsthe results with the minimum price as the dependent variable and Table 3.3 presents the results from the estimation with the maximum asking prices as dependent variable. We also show results for a standard hedonic specification without spatially lagged prices, together with some diagnostic tests for spatial dependence. The results from the spatiotemporal model reveal that spatial dependence plays an important role in the determination of land prices. The inclusion of a spatial autoregressive parameter in the model greatly enhances its explanatory performance as can be seen from the increase in the percentage of variance accounted for. In addition, the t-ratio of the spatially lagged dependent variable,
r
, is very large in both estimations, which confirms the results of the diagnostic tests for spatial dependence in the standard model. The robust LM test statistics are highly significant and point to the existence of both spatial lag and spatial error dependence. The latter may arise from omitted variables that follow a spatial pattern. The test statistic is much higher for spatial lag dependence and this suggests that spatial dependence pertains to the lagged dependent variable. The high positive value forr
suggests that the prices charged by municipalities are strongly influenced by the prices at sites in neighbouring municipalities. The coefficient estimates reveal that for any 10% increase in the weighted average price of building plots offered in neighbouring municipalities, minimum and maximum asking prices on a given site will increase by 8.3% and 8.1% respectively.40 Because the autoregressive parameter that we include in our model incorporates both temporal and spatial dependencies we can estimate the model using OLS (Can & Megbolugbe, 1997; Pace et al., 1998). Models that do not include the time dimension into the autoregressive term cannot be estimated using OLS because of the simultaneity bias problem. Such models are known as ‘spatial autoregressive’ or ‘spatial lag’ models (applications to real estate values can be found in Can, 1992; Kim, Phipps, & Anselin, 2003; Anselin & Lozano-Gracia, 2008). In a cross- sectional setting, simultaneity arises due to the two-way interaction between nearby properties. When the dataset consists of observations ordered in time, such models even have to assume that the price of a subject property is determined in part by the price of properties as yet unsold.
Notes: Regressions include year fixed effects and a dummy if land is offered leasehold. Robust (clustered
at the industrial site level) standard errors are reported. Significance levels are indicated by: * for 10 per cent, ** for 5 per cent and *** for 1 per cent.
Table 3.2
Estimation results for minimum asking prices per m2
Comparing the spatiotemporal model estimates with those for the standard model, it is apparent that the estimated coefficients are not much altered except that the local competition measure, the variable of particular interest to this study, is now significant at the 1% level of confidence. The results indicate that
competition intensity has a positive effect on asking prices. A 1% point (0.01) increase in the municipalities’ share in the total amount of land available in the local market area raises asking prices by 0.33 to 0.43%. The amount of land available for industrial and business development does appear to have a negative effect on asking prices. The estimate implies that an increase in the amount of land available for industrial development, relative to the size of the local market area, reduces asking prices. This suggests that asking prices depend critically on the presence or absence of competitive pressures between neighbouring municipalities. In local market areas where land availability and potential competition is restricted, municipalities seem to be more optimistic about the prices they can achieve for building land. In contrast, in local market areas where competition between municipalities is intense, they seem to assume lower selling prices.
VARIABLE STANDARD MODEL SPATIO-TEMPORAL MODEL