The Impact of Transit Investment on Housing Values: A Simulation Experiment

1979 ◽  
Vol 11 (3) ◽  
pp. 239-255 ◽  
Author(s):  
A Anas

This paper uses a joint-choice logit model of travel demand and residential location to simulate the impact of urban rapid-transit investment on housing values within a radial corridor. The model developed is a clean break with the traditional urban economic theory. Instead the heterogeneous nature of travel and location decisions is recognized and the logit model, consistent with stochastic utility maximization, is employed. Simulation experiments reveal that the aggregate increase in property values caused by transit's impact on work trips is highly sensitive to the aggregate number of vacancies within the corridor. Under reasonable assumptions, transit investment tends to lower central-city property values, to increase central-city vacancies, and to raise suburban property values. It tends to help the poor move further away from the center and penetrate the inner suburbs. Depending on several influences, aggregate property values can increase or decrease and the change can often be statistically insignificant. Calculations show that an equitable taxation (and compensation) of property-value changes may raise a small to modest proportion of a transit system's construction cost. Several considerations suggest that even these modest estimates might be optimistic. These results help develop an improved perspective on ‘value-capture policy’ which has not, up to now, benefited from quantitative analysis. Major extensions of the model are briefly considered.

2017 ◽  
Vol 38 (4) ◽  
pp. 427-436 ◽  
Author(s):  
Xinyu (Jason) Cao ◽  
Shengnan Lou

Studies quantifying value added of transit often cannot differentiate whether the premiums are transit effects or location effects. Limited studies have examined the timing of value added. Using before and after data, this study explores the impact of the Green Line LRT on housing sales prices. Compared to the studied period before its funding announcement, its announcement increased housing values by $9.2/sq ft and its commencement increased sales prices by $13.7/sq ft. Further analyses show that housing value appreciation actually occurred after the announcement but before the commencement. Thus, using the right timing of value added is critical for value capture programs and benefit–cost analysis.


1980 ◽  
Vol 12 (7) ◽  
pp. 747-764 ◽  
Author(s):  
A Anas

In a previous article published in this journal (Anas, 1979a), a simulation model developed by the author was used to examine the impact of transit investment on property values in an urban transportation corridor that had a completely centralized employment distribution. The present paper examines the effect of rail-transit investment in the context of various scenarios which deal with urban employment decentralization, housing distribution, transportation pricing, and income composition. From these simulations it appears that under a variety of assumptions regarding urban change the taxation of short-run differential changes in property values caused by transit investment can raise only a small portion of the cost of typical transit investments. The distinctive feature of the simulation model is that it is consistent with the discrete-choice theory of travel demand currently used in transportation planning and travel-demand prediction. But whereas the state of the art in transportation planning ignores the simultaneity of transportation changes and price changes in the housing market, the model developed here is a first attempt to deal with these effects by incorporating discrete-choice theory into a Walrasian market-equilibration procedure. In addition to being a theoretical alternative to the classical bid-rent model, still made use of by urban economists, the new approach is computationally efficient and suitable for large-scale simulation.


Author(s):  
Dorota Kmieć

The paper attempts to identify the causes of unemployment among the rural population. Logit model was used to determine the size of the impact of explanatory factors examined the situation in the labor market. The following potential predictors were considered: socio-demographic characteristics and household income, improving one’s skills through training and personal competencies.


2021 ◽  
Vol 145 ◽  
pp. 324-341
Author(s):  
Sepehr Ghader ◽  
Carlos Carrion ◽  
Liang Tang ◽  
Arash Asadabadi ◽  
Lei Zhang

1992 ◽  
Vol 7 (3) ◽  
pp. 297-314 ◽  
Author(s):  
Alan Reichert ◽  
Michael Small ◽  
Sunil Mohanty

Author(s):  
Shunhua Bai ◽  
Junfeng Jiao

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.


1987 ◽  
Vol 21 (6) ◽  
pp. 443-477 ◽  
Author(s):  
Marcel G. Dagenais ◽  
Marc J.I. Gaudry ◽  
Tran Cong Liem

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