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improvements (whether focused on transit systems or on highway systems) can best be captured by models which simulate the relationships among travel, housing and shopping decisions. Traditional travel demand models examine travel decisions separately from decisions to consume housing and to select shopping destinations. Consequently, such models do not measure how travel decisions affect rents, housing values, vacancies in the housing market, where people live after transport improvements are made, and where people travel for their shopping needs. operational tools which are designed to simultaneously model these inter­ relationships are needed in order to properly assess the full impacts and benefits of contemplated transport improvements. Such tools can be designed by adopting an appropriate microeconomic framework which synthesizes the entire bundle of travel and location decisions faced by households and other users of the transport systems. Major shifts in values among different parts of a region are caused by changes in the transport system which are related to job locations, changes in incomes and changes in costs and travel times among several modes. This is so because, ultimately, changes in values result from the aggregated travel and housing decisions of individuals. A single individual’s decision has virtually no effect on the real estate markets, but the decisions of many individuals aggregated together can exert a powerful effect which spreads throughout a region, even though these individuals may be reacting to changes which are locally confined. This spreading of effects is caused by the fact that the different parts of a region are all linked and interconnected through the economic choices available to the affected individuals.

To capture how values shift among different geographic parts of a region as well as among the different types of affected real estate, a model of the real estate markets is needed. Furthermore, since our current focus is how the value shifts are caused by personal transport decisions, such a model must be unified with a model of travel decisions. The chief objective of such models is to predict the impact of transport improvements on land and property values at the regional scale, in the aggregate, as well as by specific small subregions within a metropolitan area, and for various classes of real estate. These predictions are then used to estimate the potential aggregate revenues that can be raised by taxing the increases and, perhaps, subsidizing the decreases in values. Such taxing (or capturing by alternative means) of net value increases generated by transport improvements is known as value capture financing. (Value capture financing is discussed in more detail in the next major section of this chapter.) If models of the type discussed are to be useful, they must be of a form which is easy to replicate and operationalize for any

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