Planning Research using InfoUSA/InfoGroup Data By Qisheng Pan, Ph.D. Professor Department of Urban Planning and Environmental Policy Texas Southern University Houston, Texas February 7, 2018
Case 1: The Impacts of Light Rail on Residential Property Values: A Case Study of the Houston METRORail Transit Line
Intro trodu ductio ction • Empirical studies are not agreed on the impacts of rail transit facilities on residential property values. • Houston light rail transit, METRORail, opened to the public in January 2004. It is expected to provide commuters and transit dependent population better access to economic opportunities and social activities. However, it is not known if this transit line will have desirable impacts on various residential property values. • This study employs multi-level regression (MLR) to identify the effects of Houston’s METRORail on the corridor, using hierarchical data at two levels, i.e., individual property level and traffic analysis zone (TAZ) level. We compare the results from MLR and Ordinary Least Square (OLS) in order to try to corroborate the findings.
Previo Pr ious us St Studie ies s on the Impacts cts of Light ht Rail • Though transit rail systems, especially heavy or commuter rail, have been found to have significant positive impacts on residential property values in some cities, the estimates of values and effects of transit station proximity are widely different as reported in the literature (Huang 1994). • Due to slower speed, smaller service area, and lower portion of residential land nearby, properties near light rail stations may accrue lower premiums than those near heavy rail and commuter rail stations (Hess and Almeida 2007). • Studies of light rail report mixed results. Landis et al. (1994) found that light rail stations have positive effects on home prices to San Diego Trolley (+$2.72/meter), negative effects in San Jose (- 1.97/meter), and indiscernible effects in Sacramento.
Study St y Ar Area • The study area is defined as the traffic analysis zones (TAZs) in Harris County. • It includes the 7.5-mile light rail line and 16 stations. • The light rail service areas are classified at distances of 1/4, 1/2, 1, 2, and 3 miles from the light rail stations. • Residential properties are grouped by their locations in the various service areas.
Data • The values and physical characteristics of properties are obtained from property transaction data in InfoUSA’s 2007 residential database, which records sales price, home size, and home age for each of the properties in Harris County. Property sales price is converted to comparable values using Consumer Price Index (CPI) data obtained from the Bureau of Labor Statistics (BLS). • There are 2,028,880 property records in the InfoUSA database for Harris County in 2007. After removing redundant records, limiting transactions after 1983 (the oldest year Houston CPI data are available from BLS); selecting records with sale price information, there are 529,734 properties utilized in this study, including 800 properties located in the ¼-mile radius, 1,890 within ¼-and ½-mile, 5,390 within 1/2-and 1-mile, 12,250 within 1 and 2 miles, and 16,292 within 2 and 3 miles from light rail stations (Figure 1).
Figure 1. Properties in the study area
Table 1. Sales price and age of properties at different distances to light rail stations Distance to Light Rail Stations Sales Price ($/sqft) Home Year Less than1/4 Mile 55.24 31 1/4-1/2 mile 70.98 35 1/2 to 1 mile 68.66 40 1-2 mile 70.19 44 2-3 mile 69.91 45 Beyond 3 miles 39.23 27 Regional Average 41.31 28
Model • Most previous studies use traditional linear regression, e.g. OLS to estimate the relationship between property values and impact factors (Bowes and Ihlanfeld 2001). They ignore the different spatial levels of some variables. However, some data like property values are available at the individual point level while others are available at a more aggregated level, such as census tract, TAZ, city and county, etc. • This study employs a two-level MLR model to separate the data at individual level and group level (i.e. TAZ in this case). The results from both MLR and OLS models are compared. • To eliminate the effects of possible multicollinearity, this study tests multiple models with different sets of variables and employs MLR and OLS to examine the effects of light rail on property values, step by step.
• Employment by industrial sector by traffic analysis zone (TAZ) for the study area are available from the Census CTPP 2000 Part 2 data set. • Census CTPP 2000 also provides geographic maps of the TAZs and average travel time for OD pairs. • GIS maps are utilized to identify employment centers, calculate job accessibility, identify spatial relationships between variables, and assist computation.
Results lts • The regression results from both OLS and MLR models show that the physical characteristics of properties, including home size and home age, are the major contributors to the variance of home sales price (the R-square is about 0.32, and Pseudo R-square is 0.35, see Model 2 in Table 2). • For the light rail line, distances to rail stations and bus stops all have significant effects on the residential property values. Based on the coefficients estimated by MLR, light rail service has positive effects while bus stop has negative impacts on property values. OLS reports that both light rail and bus stop services have significant positive effects on property values. However, their effects are modest in terms of R-square (+0.04) of OLS and Pseudo R-square values (+0.02)of MLR, see Model 3 in Table 2.
Table 2. The impacts of light rail system on property values (Model 1-3) Model 1 Model 2 Model 3 Model MLR OLS MLR OLS MLR OLS Fixed eff. Level 1 Intercept 4.0774 *** 4.21874 *** -0.2949 *** -2.6274 *** -0.3010 *** -2.6826 *** lghomesize 0.6511 *** 0.9319 *** 0.6439 *** 0.9399 *** lgage -0.1586 *** -0.0895 *** -0.1567 *** -0.1258 *** Raillineop 0.0773 *** 0.0556 *** RailQMI 0.1830 *** 0.6551 *** RailHMI 0.2416 *** 0.5608 *** Rail1MI 0.1909 *** 0.4925 *** Rail2MI 0.1303 *** 0.4933 *** Rail3Mi 0.0426 *** 0.3936 *** BusStQMI -0.0186 *** 0.1410 *** lgdistcbd lgdistmed Level 2 Popdens JobDens JobAccess Random eff. Level 1 Intercept 0.3794 *** 0.3183 *** 0.3168 *** Level 2 Intercept 0.3252 *** 0.1422 *** 0.1323 *** R Square 0.3188 0.3587 Pseudo R Square 0.3464 0.3626 Obs. of Level 1 529734 529734 529734 Obs. Of Level 2 1427 1427 1427
Table 3. The impacts of light rail on property values (Model 4-6) Model 4 Model 5 Model 6 Model MLR OLS MLR OLS MLR OLS Fixed eff. Level 1 Intercept 0.2887 *** -1.9382 *** -0.3481 *** -2.6848 *** 1.1827 *** -1.4906 *** lghomesize 0.6448 *** 0.9522 *** 0.6443 *** 0.9398 *** 0.6449 *** 0.9403 *** lgage -0.1579 *** -0.1545 *** -0.1568 *** -0.1221 *** -0.1577 *** -0.1415 *** Raillineop 0.0771 *** 0.0505 *** 0.0773 *** 0.0550 *** 0.0772 *** 0.0507 *** RailQMI -0.0490 0.2241 *** 0.0876 * 0.2787 *** -0.1184 ** -0.0782 *** RailHMI 0.0373 0.1068 *** 0.1995 *** 0.5290 *** 0.0144 0.0573 *** Rail1MI 0.0090 0.0657 *** 0.1642 *** 0.4887 *** -0.0019 0.0587 *** Rail2MI 0.0097 0.1544 *** 0.1164 *** 0.4886 *** 0.0072 0.1441 *** Rail3Mi -0.0135 0.1551 *** 0.0368 *** 0.3801 *** -0.0141 0.1340 *** BusStQMI -0.0245 *** -0.0154 *** -0.0193 *** 0.1358 *** -0.0243 *** -0.0070 *** lgdistcbd 0.0121 -0.0637 *** 0.0227 -0.0622 *** lgdistmed -0.2529 *** -0.1989 *** -0.2888 *** -0.2296 *** Level 2 Popdens 0.0057 *** -0.0028 *** -0.0121 *** -0.0115 *** JobDens 0.0031 *** 0.0057 *** 0.0027 *** 0.0043 *** JobAccess -0.0004 *** -0.0001 *** Random eff. Level 1 Intercept 0.3168 *** 0.3168 *** 0.3168 *** 0.0006 Level 2 Intercept 0.1092 *** 0.1271 *** 0.1023 *** 0.0041 R Square 0.3723 0.3619 0.3763 Pseudo R Square 0.3954 0.3700 0.4052 Obs. of Level 1 529734 529734 529734 Obs. of Level 2 1427 1427 1427
Case 2: Economic Losses from a Hypothetical Hurricane Event in the Greater Houston Area
1. Introduction • Hurricanes Katrina and Rita have caused tremendous socio-economic losses on the Gulf Coast region as well as throughout areas of the United States . • Federal Emergency Management Agency (FEMA) has developed a Hurricane Model in its HAZUS package to estimate direct losses of hurricanes, however, there is no module in the hurricane model to calculate indirect and induced impacts of hurricanes on industry purchases and household consumption . • This study proposes a systematic way to estimate direct, indirect, and induced effects of hurricanes and also allocate them to small impact analysis zones . This approach is helpful for planners and decision makers to evaluate and forecast the economic losses of future hurricanes in their region so as to allocate manpower and resources for disaster planning more cautiously and efficiently.
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