Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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optimal allocation of land in the sense that each house- hold chooses a home that most satisfies it while develop- ers and land owners maximize profits and rents. The spatial distribution of households and the equilibrium home prices are endogenously determined as the out- come of a housing- market mechanism involving land and transport. DATA AND METHODS This section describes the data used to calibrate the loca- tion choice model and to reach single- family housing market equilibriums. Both procedures were coded in GAUSS matrix programming language (Aptech Systems 2003). Location Choice Model Bina and Kockelman (2006) undertook a survey of Austin movers in 2005. Sampling half of Travis County’s recent [with “recent” meaning within the past 12 months (before the sampling date and start of the survey)] home buyers, responses were obtained from over 900 house- holds, or roughly 12% of those buyers. The data set con- tains comprehensive information on household dem- ographics, housing characteristics, reasons for reloca- tion, and preferences when facing different housing and location choice scenarios. Commute distance and cost have a bearing on one’s residential location choice (e.g., Van Ommeren et al. 1999, Rouwendal and Meijer 2001, Clark et al. 2003, Tillema et al. 2006). The GIS- encoded addresses of homes and workplaces, accompanied by roadway network data, provide a direct measure of commute time (with com- mute time calculated by using Caliper’s TransCAD soft- ware for shortest travel- time path under free- flow conditions) for all potential locations. Because value of travel time (VOTT) was not directly available in the data set, it was approximated as the average wage (with part- time employed persons assumed to work 1,000 h/year, while full- time employed persons were assumed to work 2,000 h/year) of each household’s employed members and was assumed to be equal over the household’s employed members. In addition to work access, each potential home’s (Euclidean) distance to the nearest of the region’s largest 18 shopping centers (DISTMALL) helps explain the impact of shopping access on location choice. Furthermore, household annual (pretax) income (HHINC), home size (SQFOOT), and housing prices per interior (built) square foot (UNITP) help explain the bal- ance of home affordability [where price (equal to UNITP  SQFOOT) is divided by annual income] and house- holds’ preferences for larger home sizes. Residential location choice was modeled via a multi - nomial logit framework. The random utility was speci- fied as follows: where Uhi = random utility of household h for choosing home i, β1, β1, . . . , β7 = parameters to be estimated, Nh = number of workers in household h, VOTTh = household’s approximate value of travel time, TThin = network commute time for worker n in household h when residing in home i, DISTWORKhin = corresponding Euclidean distance, and εhi = random component assumed to be independent identically distributed (IID) Gumbel, across households h and their alternatives i. For model calibration, each household’s choice set con- sisted of 20 alternatives: 19 randomly drawn from the pool of all homes purchased by respondents in the recent mover survey plus the chosen option. These model results are shown in Table 1. The model indicates a concave relationship between strength of preference (systematic utility) and the ratio of home price to annual income. The parameter values on the ratio and its squared term suggest that more expensive homes are preferred when the ratio is less than 1.7, becoming less attractive as the ratio of price to income exceeds this threshold. Larger homes, of course, are more desired, with SQFOOT increasing the likelihood of a home’s selection, everything else constant. The negative signs associated with commute costs and Euclidean distances to workers’ workplaces support the notion that households favor homes closer to their employed workers’ jobs. Major mall access, however, was not favored; perhaps the potentially high volumes of traffic and congestion in the vicinity of major shopping centers offset any possible access gains. Other forms of shopping access may be desired but require geocoding of far more smaller shopping. 64 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2 U h i i i h i = + × + ×β β β 1 2 3 UNITP SQFOOT HHINC UNITP SQFOOT HHINC SQFOOT V i h i ⎛ ⎝⎜ ⎞ ⎠⎟ + + 2 4 51 000, β β OTT TT DISTWORK h hin n N hin n h × + ( ) ( ) = ∑ 1 6 β = ∑ + + 1 7 N i hi h β εDISTMALL