APPENDIX D. RESIDENTIAL AND FIRM LOCATION CHOICE MODEL SPECIFICATION AND ESTIMATION
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1 APPENDIX D. RESIDENTIAL AND FIRM LOCATION CHOICE MODEL SPECIFICATION AND ESTIMATION This document summarizes the latest location choice estimation for Greater Boston Land Use model. It contains residential location choice, residential rent model, and firm location choice model. The residential location choice and rent models are updated on Dec 14, The firm location choice models are updated on May 2, They are implemented in Cube Land for the Greater Boston Integrated Transport and Land Use model (BosLUT). [Contributors: Menghan Li, Pablo Posada, and Victor Rocco] 1. Residential location choice model 1.1 Specification Bidding = log(rooms)+is_mf+i(jobs_tot_pt_am / Jobs_tot_AUTO_AM)+ Acc_HH1 +Acc_HH8 + Acc_HH11 + pop_den + race+ log(mid_inc) 1.2 Variables -rooms: number of rooms of housing unit -is_mf: multi-family -(Jobs_tot_PT_AM / Jobs_tot_AUTO_AM): Ratio of transit accessibility to total jobs to auto accessibility to total jobs (accessibility measured by cross-product of jobs and friction factor) -Acc_HH1, Acc_HH8, Acc_HH11: accessibility to group 1, 8, 11 -pop_den: zonal population density (people/ha) -race: white people ratio -mid_inc: zonal median income/ Variables that have been tried but are not significant in the bidding function: - SAT score - FAR: average floor area ratio in the zone - Share of Recreational Space - Share of Conservative Space - Distance to Recreational Space - Distance to Recreational Space - Distance to coast/ dummy variable if the zone is adjacent to the coast - Zonal employment density 1
2 - Distance to MBTA station/dummy variable if the distance to T is less than 1km - Many measures of accessibility, including ~ accessibility to downtown jobs by auto ~ accessibility to all households by auto ~ many other decay functions/travel times/combinations 1.4 Group definition Agent ID obs. Description 1 73 Young (15-34), low inc, size<= Young (15-34), low inc, size>= Young (15-34), high inc, size<= Young (15-34), high inc, size>= Mid-age (35-64), low inc, size<= Mid-age (35-64), low inc, size>= Mid-age (35-64), high inc, size<= Mid-age (35-64), high inc, size>= Old (65+), low inc, size<= Old (65+), low inc, size>= Old (65+), high inc, size<= Old (65+), high inc, size>=3 1.5 Result 1- didn t fix non-significant coefficients: (file: share_lu model/estimation/residential/ _finals/ model_17_accs+den~2.html) Number of estimated parameters: 110 Number of observations: 5119 Number of individuals: 5119 Null log-likelihood: Cte log-likelihood: Init log-likelihood: Final log-likelihood: Likelihood ratio test: Rho-square: Adjusted rho-square: Final gradient norm: e-03 Diagnostic: Iterations are stucked Iterations: 189 Run time: 07:12 2
3 Variance-covariance: from analytical hessian Sample file: Sample_Acc_FF_PA_ dat Table 1 Model Estimation Results Name Value Rob. std err Rob. t-test Rob. p-val ASC_1 0 ASC_ * ASC_ ASC_ ASC_ * ASC_ * ASC_ ASC_ * ASC_ * ASC_ * ASC_ ASC_ * bacctot_pa_ratio1 0 bacctot_pa_ratio * bacctot_pa_ratio bacctot_pa_ratio bacctot_pa_ratio * bacctot_pa_ratio * bacctot_pa_ratio bacctot_pa_ratio bacctot_pa_ratio bacctot_pa_ratio bacctot_pa_ratio bacctot_pa_ratio * bacc_hh11_1 0 bacc_hh11_ * bacc_hh11_ * bacc_hh11_ bacc_hh11_ * bacc_hh11_ * bacc_hh11_ * bacc_hh11_ * bacc_hh11_ * bacc_hh11_ * bacc_hh11_ * bacc_hh11_ * 3
4 bacc_hh1_1 0 bacc_hh1_ * bacc_hh1_ * bacc_hh1_ * bacc_hh1_ * bacc_hh1_ * bacc_hh1_ * bacc_hh1_ bacc_hh1_ bacc_hh1_ * bacc_hh1_ * bacc_hh1_ bacc_hh8_1 0 bacc_hh8_ * bacc_hh8_ * bacc_hh8_ * bacc_hh8_ * bacc_hh8_ * bacc_hh8_ * bacc_hh8_ * bacc_hh8_ bacc_hh8_ * bacc_hh8_ * bacc_hh8_ * binc_log1 0 binc_log * binc_log binc_log * binc_log * binc_log * binc_log binc_log * binc_log * binc_log binc_log binc_log * bismf1 0 bismf bismf bismf * bismf bismf * bismf
5 bismf bismf bismf bismf bismf bpop_den1 0 bpop_den * bpop_den * bpop_den bpop_den * bpop_den * bpop_den * bpop_den * bpop_den * bpop_den * bpop_den * bpop_den * brace1 0 brace * brace * brace * brace * brace * brace * brace * brace * brace * brace * brace * broom_log1 0 broom_log broom_log broom_log broom_log * broom_log * broom_log broom_log * broom_log broom_log broom_log broom_log * 5
6 1.6 Result 2- fix non-significant coefficients directory: share_lu model/estimation/residential/ _fix_non_significant/model_18_accs~9.html Model: Logit Number of estimated parameters: 66 Number of observations: 5119 Name Number of individuals: 5119 Null log likelihood: Cte log likelihood: Init log likelihood: Final log likelihood: Likelihood ratio test: Rho-square: Adjusted rho-square: Final gradient norm: e-004 Diagnostic: Normal termination. Obj: e-006 Const: e-006 Iterations: 111 Run time: 02:50 Variance-covariance: from analytical hessian Sample file: Sample_Acc_FF_PA_ dat Table 2 Model Estimation Results Value ASC_1 0 Robust Std err Robust t-test p-value ASC_ ASC_ ASC_ ASC_2 0 ASC_ ASC_ ASC_ ASC_ ASC_ ASC_ ASC_ bacctot_pa_ratio1 0 bacctot_pa_ratio bacctot_pa_ratio bacctot_pa_ratio bacctot_pa_ratio2 0 bacctot_pa_ratio bacctot_pa_ratio
7 bacctot_pa_ratio bacctot_pa_ratio6 0 bacctot_pa_ratio bacctot_pa_ratio bacctot_pa_ratio9 0 bacc_hh11_1 0 bacc_hh11_10 0 bacc_hh11_ bacc_hh11_ bacc_hh11_2 0 bacc_hh11_ bacc_hh11_ bacc_hh11_ bacc_hh11_ bacc_hh11_ bacc_hh11_8 0 bacc_hh11_ bacc_hh1_1 0 bacc_hh1_ bacc_hh1_ bacc_hh1_12 0 bacc_hh1_2 0 bacc_hh1_3 0 bacc_hh1_4 0 bacc_hh1_ bacc_hh1_ bacc_hh1_ bacc_hh1_8 0 bacc_hh1_ bacc_hh8_1 0 bacc_hh8_ bacc_hh8_ bacc_hh8_12 0 bacc_hh8_ bacc_hh8_ bacc_hh8_4 0 bacc_hh8_ bacc_hh8_ bacc_hh8_7 0 bacc_hh8_ bacc_hh8_ binc_log1 0 binc_log10 0 7
8 binc_log binc_log binc_log2 0 binc_log binc_log binc_log5 0 binc_log binc_log binc_log binc_log bismf1 0 bismf bismf bismf bismf bismf bismf bismf bismf bismf bismf bismf bpop_den1 0 bpop_den10 0 bpop_den11 0 bpop_den12 0 bpop_den2 0 bpop_den3 0 bpop_den4 0 bpop_den5 0 bpop_den6 0 bpop_den7 0 bpop_den8 0 bpop_den9 0 brace1 0 brace10 0 brace11 0 brace12 0 brace2 0 brace3 0 brace4 0 brace5 0 brace6 0 8
9 brace7 0 brace8 0 brace9 0 broom_log1 0 broom_log broom_log broom_log broom_log2 0 broom_log3 0 broom_log broom_log5 0 broom_log broom_log broom_log broom_log9 0 Coefficient in red: fixed 2. Residential Rent model Updated: Specification and result Table 3 Model Estimation Results Estimate Std. Error t value Pr(> t ) (Intercept) -2.69E E E+01 < 2e-16 *** logsum 7.24E E E+01 < 2e-16 *** Jobs_tot_AUTO_AM 6.78E E E+01 < 2e-16 *** Rec_share 5.20E E E E-06 *** crime -3.35E E E E-08 *** SAT_ratio 1.08E E E E-10 *** mid_inc 5.32E E E+00 < 2e-16 *** race 6.57E E E+01 < 2e-16 *** FAR 1.58E E E *** data$dis_mbt <= 1000TRUE 2.29E E E E-10 *** Acc_HH_tot -7.37E E E E-11 *** Signif. codes: 0 *** ** 0.01 * Residual standard error: on 3772 degrees of freedom Multiple R-squared: , Adjusted R-squared:
10 F-statistic: on 10 and 3772 DF, p-value: < 2.2e Variable explanation - logsum: expected highest bid - Jobs_tot_AUTO_AM: auto accessibility to total jobs during AM peak hour - Rec_share: share of recreational space within the zone - (COAST <= 2000: dummy variable that equals to 1 if the distance from zonal centriod to the coast is no greater than 2000)---taken out in this version - crime: crime index = town-level (violate crime rate + 0.8*property crime rate)/max(violate crime rate + 0.8*property crime rate) - SAT_ratio: SAT score of the school district/highest SAT score at school district level - mid_inc: median zonal income - race: white people ratio in the zone - FAR: average floor area ratio in the zone - dis_mbt: distance from TAZ centroid to closest MBTA - Acc_HH_tot: accessibility to all households by auto during AM peak hour 2.3 Variables that have been tried but are not significant in rent model - population density in the zone - employment density in the zone - distance to recreational space - log form or dummy form of distance to MBTA/Recreational Space - log form of median income 2.4 Other notes -Acc_HH1, Acc_HH2, Acc_HH12 are significant in the rent model, but I didn't include because it's very hard to interpret why all people want to live close to group 1 and far from group 2, Multi-collinearity check logsum Table 4 Correlation Matrix of Explanatory Variables Jobs_tot_ AUTO_AM Rec_s hare X.COAST crime SAT_r atio mid_inc race FAR X.data.d is_mbt logsum Jobs_tot_AUTO_AM Rec_share X.COAST crime SAT_ratio Acc_H H_tot 10
11 mid_inc race FAR X.data.dis_MBT Acc_HH_tot Firm location choice model Updated: Specification B = acc_hwy + acc_pop + acc_emp + ind_den_1 + ind_den_2 + ind_den_6 + ind_den_11 + log(mid_inc+1) + SF_C + WareH + far + LoDen + HiDen 3.2 Variables acc_hwy: accesibility to highway, calculated by distance to specific highway entries ("Limited" zonal variables from MAPC) acc_pop: AM driving accessibility to 2727 zonal population, calculated by gravity model with friction factor acc_emp: AM driving accessibility to 2727 zonal employment, calculated by gravity model with friction factor ind_den_1, ind_den_2, ind_den_6, ind_den_11: density of jobs in industry sector 1, 2, 6, 11 log(mid_inc+1): log transform of (zonal median income/1000), log(mid_inc+1)=ln(mid_inc/1000+1) SF_C: dummy for sites 10,000-39,999 square feet (MAPC) WareH: dummy for warehouse, industrial, utilities land use type (MAPC) far: real estate far (MAPC) LoDen: Real estate product variable from MAPC, dummy variable indicating whether the parcel is of the low density retail, entertainment, services, medical, office, hospitality land use type HiDen: Real estate product variable from MAPC, dummy variable indicating whether the parcel is of the high density retail, entertainment, services, medical, office, hospitality land use type 3.3 Variables that have been tried but are not significant in the bidding function Acc_HH: accessibility to total household, correlated with acc_pop 11
12 3.4 Group definition Table 5 Group Definitions for the Firms Agent ID Obs. Agent Description (Ref) Construction Health and Education Finance 4 89 Government Information Retail & Leisure Manufacture 8 5 Nat Res and Extraction Other Services Professional Serv Utility/Transp/Wholesale 3.5 Result (file: share_lu model/estimation/non- Residential/ _rejoin2_MAPCdata/Firms_2010_CL_simplify~1) Model: Multinomial Logit Number of estimated parameters: 140 Number of observations: 7486 Number of individuals: 7486 Null log-likelihood: Cte log-likelihood: Init log-likelihood: Final log-likelihood: Likelihood ratio test: Rho-square: Adjusted rho-square: Final gradient norm: e-04 Diagnostic: Normal termination. Obj: e-06 Const: e-06 Iterations: 239 Run time: 23:39 Variance-covariance: from analytical hessian Sample file: Firms_2010_BG_ dat 12
13 Table 6 Model Estimation Results for Firm Location Choice Model Name Value Rob. p-val ASC_1 0 ASC_ * ASC_ * ASC_ * ASC_ * ASC_ * ASC_ * ASC_ * ASC_ * ASC_ * ASC_ * HiDen1 0 HiDen HiDen * HiDen * HiDen HiDen * HiDen HiDen * HiDen * HiDen * HiDen * LoDen1 0 LoDen LoDen * LoDen * LoDen * LoDen * LoDen LoDen * LoDen * LoDen * LoDen * SFC_is_C1 0 SFC_is_C SFC_is_C SFC_is_C SFC_is_C SFC_is_C SFC_is_C SFC_is_C
14 SFC_is_C SFC_is_C SFC_is_C WareH1 0 WareH WareH * WareH WareH WareH WareH WareH WareH * WareH WareH acc_emp1 0 acc_emp acc_emp * acc_emp * acc_emp3 4.62E * acc_emp * acc_emp acc_emp * acc_emp acc_emp * acc_emp * acc_hwy1 0 acc_hwy * acc_hwy * acc_hwy * acc_hwy acc_hwy * acc_hwy * acc_hwy * acc_hwy * acc_hwy * acc_hwy * acc_pop1 0 acc_pop * acc_pop * acc_pop acc_pop * acc_pop * acc_pop * 14
15 acc_pop * acc_pop * acc_pop * acc_pop * far1 0 far * far * far * far * far * far * far * far far * far * gsize1 1 gsize10 1 gsize11 1 gsize2 1 gsize3 1 gsize4 1 gsize5 1 gsize6 1 gsize7 1 gsize8 1 gsize9 1 ind_den1_1 0 ind_den1_ * ind_den1_ * ind_den1_ * ind_den1_ * ind_den1_ * ind_den1_ * ind_den1_ ind_den1_ * ind_den1_ * ind_den1_ * ind_den2_1 0 ind_den2_ * ind_den2_ * ind_den2_ ind_den2_ * ind_den2_
16 ind_den2_ * ind_den2_ * ind_den2_ * ind_den2_ * ind_den2_ * ind_den6_1 0 ind_den6_ ind_den6_ * ind_den6_ * ind_den6_ ind_den6_ ind_den6_ * ind_den6_ ind_den6_ * ind_den6_ * ind_den6_ log_mid_inc1 0 log_mid_inc * log_mid_inc * log_mid_inc log_mid_inc * log_mid_inc log_mid_inc log_mid_inc log_mid_inc * log_mid_inc * log_mid_inc * 16
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