Planning Research using InfoUSA/InfoGroup Data

Transcription

1 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

2 Case 1: The Impacts of Light Rail on Residential Property Values: A Case Study of the Houston METRORail Transit Line

3 Introduction 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 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.

4 Previous Studies on the Impacts of Light 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.

5 Study 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.

6 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 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).

7 Figure 1. Properties in the study area

8 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 /4-1/2 mile /2 to 1 mile mile mile Beyond 3 miles Regional Average

9 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.

10 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.

11 Results 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.

12 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 *** *** *** *** *** *** lghomesize *** *** *** *** lgage *** *** *** *** Raillineop *** *** RailQMI *** *** RailHMI *** *** Rail1MI *** *** Rail2MI *** *** Rail3Mi *** *** BusStQMI *** *** lgdistcbd lgdistmed Level 2 Popdens JobDens JobAccess Random eff. Level 1 Intercept *** *** *** Level 2 Intercept *** *** *** R Square Pseudo R Square Obs. of Level Obs. Of Level

13 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 *** *** *** *** *** *** lghomesize *** *** *** *** *** *** lgage *** *** *** *** *** *** Raillineop *** *** *** *** *** *** RailQMI *** * *** ** *** RailHMI *** *** *** *** Rail1MI *** *** *** *** Rail2MI *** *** *** *** Rail3Mi *** *** *** *** BusStQMI *** *** *** *** *** *** lgdistcbd *** *** lgdistmed *** *** *** *** Level 2 Popdens *** *** *** *** JobDens *** *** *** *** JobAccess *** *** Random eff. Level 1 Intercept *** *** *** Level 2 Intercept *** *** *** R Square Pseudo R Square Obs. of Level Obs. of Level

14 Case 2: Economic Losses from a Hypothetical Hurricane Event in the Greater Houston Area

15 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.

16 2. Methodology This study employs the damage functions in HAZUS for estimating the property damage of a hypothetical hurricane on buildings, contents, and inventories by industrial sectors. It transforms the property damage to the direct losses of employment and productions, calculates total indirect and induced effects using a regional input-output model. It allocates direct, indirect, and induced effects to impact analysis zones in the region.

17 Storm track, wind speed, central pressure, radius to max winds, and other parameters for a hurricane Hurricane Model Building damage state probabilities by occupancy class by census tract Building recovery time for different damage states by occupancy class Commercial building and service interruption time by occupancy class by census tract Small area employment data by industrial sector Job losses due to business interruption Dollar/job ratios Output losses due to business interruption Figure 1. Procedures to Calculate Direct Impacts of a Hurricane Event

18 3. Empirical Study This study creates a scenario for a hypothetical major hurricane event in the Houston-Galveston area. It estimates building damage for this designated hurricane using the Hurricane Model in the FEMA s HAZUS-MH package. It calculates direct, indirect and induced effects by industrial sector in small areas. It highlights the most vulnerable geographic areas in the region.

19 Figure 2. Hurricane Data Available in the HURREVAC 2000 Database

20 Figure 3. Storm Track and Advisory Points for the Hypothetical Hurricane Event

21 Employment Data from InfoUSA To estimate the impacts of a hurricane, this study utilizes the 2005 InfoUSA business databases for Houston-Galveston Area Council (H- GAC) region, which were obtained from the Houston Geographic Data Committee (GDC) The data includes the number of jobs by Standard Industrial Classification (SIC) code and North American Industry Classification System (NAICS) code for the 207,690 business establishments in the H-GAC region. These businesses are represented as points in the data file compatible to Geographic Information System (GIS) format, which allows point-level employment data to be easily superimposed on other geo-referenced data. The employment by business location is aggregated to census tracts and the SIC codes are aggregated to a small number of industrial sectors to facilitate later analysis.

22 4. Results Figure 4. Total Losses of Jobs from the Hypothetical Hurricane Event

23 Table 1. Output and Job Losses from the Hypothetical Hurricane Event Output ($1,000s) Jobs Direct Indirect Induced Total Direct Indirect Induced Total City of Houston 5,188,013 2,425,319 2,813,600 10,426,932 40,201 15,858 30,010 86,069 Brazoria County 790, ,893 1,231,385 2,169,555 5, ,134 19,631 Chambers County 7,827 25, , Fort Bend County 238, , , ,905 1,334 1,550 2,047 4,931 Galveston County 962, ,934 2,420,944 3,515,061 8, ,822 34,960 Harris County 7,209,271 3,359,015 4,614,983 15,183,269 54,365 21,858 49, ,447 Liberty County , , Montgomery County 17, ,563 4, , , ,540 Waller County 44 17, , Sum of Eight Counties 9,225,454 4,256,717 8,465,213 21,947,383 69,671 27,039 90, ,000 Regional Leakages 3,724,930 1,529,657 3,104,553 8,359,140 25,302 10,236 32,981 68,519 REGIONAL TOTAL 12,950,384 5,786,373 11,569,766 30,306,523 94,973 37, , ,518 Source: Author Calculations.

24 Table 2. The 20 Cities with the Highest Losses of Output from the Hypothetical Hurricane Event Output ($1,000s) Jobs Index City Name Direct Indirect Induced Total Direct Indirect Induced Total 1 Houston 5,188,013 2,425,319 2,813,600 10,426,932 40,201 15,858 30,010 86,069 2 Pasadena 448,527 94, ,558 1,024,496 3, ,136 8,850 3 Galveston 260,244 44, , ,886 2, ,699 8,694 4 Texas City 265,844 31, , ,776 1, ,955 6,039 5 League City 123,143 16, , ,017 1, ,321 6,989 6 Pearland 139,341 20, , ,084 1, ,592 4,918 7 Friendswood 68,162 8, , , ,350 4,216 8 Alvin 130,112 16, , ,318 1, ,441 2,551 9 Deer Park 105,656 19, , , ,623 2, La Marque 93,233 10, , , ,733 2, La Porte 95,386 20, , , ,566 2, Webster 175,369 15,536 58, ,925 1, , Stafford 105,282 72,000 5, , Sugar Land 50, ,632 17, , Hitchcock 41,856 4, , , ,165 1, Missouri City 46,421 24,405 60, , , The Woodlands 9, ,853 2, , Channelview 64,023 21,823 36, , Manvel 17,234 2,114 96, , ,032 1, Seabrook 40,568 5,592 59, , TOTAL 7,468,172 3,069,980 6,391,157 16,929,309 58,054 19,348 68, ,570

25 Case 3: Economic Losses from Hurricane IKE in the Greater Houston Area

26 Introduction When Hurricane IKE made its US landfall in Galveston, Texas at 2:10am CDT on September 13, 2008, its wind speed was 110 mile per hour (MPH), the maximum of a Category 2 hurricane. It also created a maximum of 15-foot storm surge at the Texas coast. Hurricane IKE caused a variety of physical damages on its way right crossing the heart of the Houston region. High wind ripped away roofs, tumbled walls, broke windows, and tore off exterior materials of houses and buildings. Severe damages were found in many tall buildings in the center of the city. Though the magnitude of the hurricane was less than the expectation, it still caused significant property damage and economic losses to the Gulf Coast region and directly and indirectly throughout much of the U.S.

27 Economic losses of residents and businesses have extended for a long period time after Hurricane IKE. The widespread damages on residential houses, commercial buildings, transport facilities, and utility service have taken the regional much longer than usual to recover. The Houston Chronicle on October 21, 2008 reported that over half of the 2,000 apartment complexes in Houston were hardly hit by Hurricane IKE and about 150 of these apartment complexes were severely damaged. Overall three quarter of households in Houston had suffered from power cuts. On September 25, 13 days after the strike of the hurricane, there was still about one quarter of the residents in the city having no electricity.

28 An important step of disaster relief is to have a quick estimate of hurricane damage losses, which may help government agencies to identify most cost-effective options for disaster mitigation and allocate manpower and resources for disaster relief more efficiently. The report for Hurricane IKE became available in the early of 2009, about four months after the Hurricane IKE made its US landfall. National Hurricane Center (NHC) at Miami has released Tropical Cyclone Report for major hurricanes striking the U.S. These estimates of uninsured losses is based on empirical studies and the method to calculate the total losses by doubling the insured damage costs seems too arbitrary. In addition, the NHC s reports have no information about the spatial distribution of the damage losses in small areas.

29 Figure 2. Storm Track of Hurricane IKE and Hypothetical Hurricane RITA.

30 Figure 3. Estimated Wind Speeds of Hurricane IKE by HAZUS-MH.

31 Figure 4. Estimated Property Damage Losses of Hurricane IKE

32 As Pan et al. (2008) suggested, building damage states are applied to estimate business interruption times and employment data is utilized to calculate the business interruption losses, which is denoted by Equation (1). The business interruption times are calculated by the building recovery time for the corresponding building damage states and the building interruption time multipliers, both available from HAZUS manual (FEMA 2006, 2006b). They are converted to an annual ratio to be consistent to employment data and the annual data used in the input-output model. Employment data come from the InfoUSA 2008 business database, which provides the number of jobs by standard industrial code (SIC) for Houston-Galveston Area Council (H-GAC) region. Employment data enters Equation (1) with the building recovery times to yield the economic losses of jobs by sector by census track, which are the direct impacts. Dollar per job ratios are utilized to covert economic job losses to losses in dollar value.

33 An economic input-output model, i.e. IMPLAN in this study, is employed to calculate indirect and induced effects using the direct impacts in dollar or job. Multiple programs are developed to bridge the employment sectors and the sectors in IMPLAN 2000 sectors. The indirect impacts of the IMPLAN input-output model are not listed by impact analysis zone. Equation (2) with employment data by census tract is utilized to allocate total indirect impacts. Similar to indirect impacts, the induced effects as the output of the IMPLAN input-output model are the regional total without spatial information. A journey home-to-work O-D matrix and a journey home-to-work O-D matrix from the H-GAC 2000 base year model are used to allocate the induced effects to the 2954 H-GAC TAZs, which are further migrated to the 866 census tract.

34 The results of direct, indirect, and induced effects measured by both dollar value and job for City of Houston and eight H-GAC Counties are listed in Table 2. It reports $11.6 billion of output losses and 97,374 persona-years of employment losses, which are combined with the $20.6 billion of property damage to make Hurricane IKE the third most destructive and costliest hurricane in the U.S. (lower than the losses in Hurricane Andrew of 1992 and Hurricane Katrina of 2005). Table 2 also shows that regional leakages, i.e. spillovers in the indirect and induced effects, are very large ($8.1 billion of output and 55,449 person-years of employment), which reflects the fact that the local component of the manufacturing and mining sectors is very high, with heavy reliance on trades with outside the region.

35 Table 2. Estimated Output and Job Losses from Hurricane IKE Output ($1,000s) Jobs Direct Indirect Induced Total Direct Indirect Induced Total City of Houston 491, , ,804 1,251,914 6,616 2,649 5,265 14,530 Brazoria County 48,813 14,217 90, , ,908 Chambers County 38,379 4,883 21,610 64, Fort Bend County 1,038 33, , , ,659 2,044 Galveston County 351,852 24, , ,416 5, ,186 7,033 Harris County 1,058, , ,820 2,396,117 14,366 3,710 10,293 28,369 Liberty County 7,160 1,160 12,625 20, Montgomery County 19,727 19,346 75, , ,351 Waller County ,455 4, Sum of Eight Counties 1,525, ,187 1,444,211 3,430,343 21,816 4,757 15,353 41,926 Regional Leakages 3,422,245 1,785,626 2,926,258 8,134,128 14,515 9,731 31,201 55,449 TOTAL 4,948,190 2,245,813 4,370,469 11,564,472 36,331 14,488 46,554 97,374 Source: Author Calculations.

36 Among the eight counties in the H-GAC region, Harris County accounts for over two third of the losses in output and employment. Galveston County, where Hurricane IKE made landfall, ranked second by losing $483 million of output and over 7,000 jobs. Brazoria County, located in the south of the region, ranked third in terms of direct output and job losses. The spatially allocated output losses are shown in Figure 5. Table 3 shows the tops 20 cities in the region ranked by their losses of output in Hurricane IKE. City of Houston, the biggest city in the region, has the largest impacts on the list, losing about $1.3 billion output and 14,530 jobs, which are about seven times larger than the second city on the list, City of Pasadena, and also one third of the regional losses excluding the leakage. City of Galveston, regardless of its size, ranks the third on the list and losses $144.9 million of output and 1,856 jobs. All the rest cities among the top ten are located in the south of the region along the storm tract.

37 Figure 5. Estimated total losses of output from Hurricane IKE Event

38 Table 3. The 20 Cities with the Highest Losses of Output from Hurricane IKE Event Output ($1,000s) Jobs Index City Name Direct Indirect Induced Total Direct Indirect Induced Total 1 Houston 491, , ,804 1,251,914 6,616 2,649 5,265 14,530 2 Pasadena 124,673 9,580 39, ,590 1, ,216 3 Galveston 125,630 5,441 13, ,909 1, ,856 4 Baytown 95,548 2,907 13, ,280 1, ,414 5 Webster 88,283 1,919 3,115 93,317 1, ,220 6 League City 59,064 2,446 16,874 78,385 1, ,509 7 Texas City 37,280 3,413 13,850 54, La Porte 31,690 2,406 10,403 44, Pearland 27,060 2,462 14,363 43, Deer Park 25,011 2,428 9,823 37, The Woodlands 5,091 7,720 21,781 34, Atascocita 21,382 1,360 9,794 32, Friendswood 22,402 1,103 8,357 31, Sugar Land 386 8,413 22,810 31, Channelview 19,993 2,228 7,791 30, Missouri City 201 3,195 20,194 23, Alvin 11,725 1,639 4,742 18, Conroe 3,937 3,850 8,292 16, Nassau Bay 12, , Aldine 7,171 2,233 4,524 13, TOTAL 1,210, , ,218 2,280,858 17,091 3,318 7,890 28,299 Source: Author Calculations.

39 CONCLUSIONS AND DISCUSSIONS Disaster relief plans have to base on a careful study on damage states and economic losses in each impact zone, which are not limited to the areas under direct attack. To gain a better estimate of the economic losses from natural disasters, this paper develops a systematic method with a general framework that is a combination of disaster model, economic inputoutput model, and spatial allocation model. The method complements the HAZUS Hurricane Model with a more reasonable estimation of direct output and job losses and fills the gaps by developing the functions to estimate indirect and induced losses. It also utilizes GIS spatial analysis functions to allocate the losses to multiple types of impact analysis zones or political jurisdictions.

40 This method is implemented to investigate the economic losses of Hurricane IKE in the Houston-Galveston region. It reports that Hurricane IKE has caused significant economic losses to the Houston region. Its property damage and the business interruption losses have made Hurricane IKE the third costliest one in the U.S. of all time. The property damage and economic losses are not limited to the corridor along the storm track. The losses have widely extended from the south coast in the Galveston Bay to the Montgomery County in the north of the region. Some areas such as the ones in Fort Bend County receive few direct impacts but a fair amount of induced impacts, which makes the total impacts large. The results are in line with the observation from the real world. The quick and proper estimate of hurricane damage losses will help policy maker and planners to identify most cost-effective options for disaster and mitigation and allocate manpower and resources for disaster relief more efficiently.

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42 My recent publications using InfoUSA/InfoGroup data Pan, Q. (2015) "Estimating the Economic Losses of Hurricane IKE in the Greater Houston Region," Nat. Hazards Rev., Vol.16, Issue 1, Pan, Q., H. Pan, M. Zhang, and B. Zhong (2014) The Effects of Rail Transit on Residential Property Values: A Comparison 1 Study on the Rail Transit Lines in Houston and Shanghai, Transportation Research Record (TRR), Journal of the Transportation Research Board, No. 2453, pp Pan, Q. (2013) The Impacts of an Urban Light Rail System on Residential Property Values: A Case Study of the Houston METRORail Transit Line, Journal of Transportation Planning and Technology, Vol. 36, No. 2, Pan, Q. (2011) Economic Losses from a Hypothetical Hurricane Event in the Houston-Galveston Area, Natural Hazard Review, Vol. 12, No. 3,