Forecasting Housing Prices under Different Submarket Assumptions

This research evaluated forecasting accuracy of hedonic price models based on a number of different submarket assumptions. Using home sale data for the City of Knoxville and vicinities merged with geographic information, we found that forecasting housing prices with submarkets defined using expert knowledge and by school district and combining information conveyed in different modeling strategies are more accurate and efficient than models that are spatially aggregated, or with submarkets defined by statistical clustering techniques. This finding provided useful implications for housing price prediction in an urban setting and surrounding areas in that forecasting models based on expert knowledge of market structure or public school quality and simple model combining techniques may outperform the models using more sophisticated statistical techniques.Clustering, Forecasting, Hedonic price, Housing Submarket, Demand and Price Analysis, C53, R21,

Land Value Capture Modeling in Commercial and Office Areas using a Big Data Approach

Infrastructure development in Indonesia creates massive impacts on the economy. The Light rail transit (LRT) of greater Jakarta (Jabodebek) project has been estimated to have cost more than 29 trillion rupiahs due to land acquisition and route planning. The urban transit development may impact to the price of property including residential, commercials and offices along the route. This research aims to determine variables affecting the price elasticity of property and the correlation to station proximity. Data mining through web scrapping was used to assess the degree of correlation between price elasticity and station location. The result shows that approximately 13% of the commercial property was spread over a distance of 1 km from the LRT station. The closer a property to transit station, the price will be twice cheaper compared to those located further. The findings also show variables that highly contribute to property prices including schools, hospitals, and proximity to some of transit stations located in city center of Jakarta and building density

A Spatial and Temporal Autoregressive Local Estimation for the Paris Housing Market

This original study examines the potential of a spatiotemporal autoregressive Local (LSTAR) approach in modelling transaction prices for the housing market in inner Paris. We use a data set from the Paris Region notary office (“Chambre des notaires d’Île-de-France”) which consists of approximately 250,000 transactions units between the first quarter of 1990 and the end of 2005. We use the exact X -- Y coordinates and transaction date to spatially and temporally sort each transaction. We first choose to use the spatiotemporal autoregressive (STAR) approach proposed by Pace, Barry, Clapp and Rodriguez (1998). This method incorporates a spatiotemporal filtering process into the conventional hedonic function and attempts to correct for spatial and temporal correlative effects. We find significant estimates of spatial dependence effects. Moreover, using an original methodology, we find evidence of a strong presence of both spatial and temporal heterogeneity in the model. It suggests that spatial and temporal drifts in households socio-economic profiles and local housing market structure effects are certainly major determinants of the price level for the Paris Housing Market.Hedonic Prices; Heterogeneity; Paris Housing Market; STAR Model

(WP 2010-02) The Demand for Historic Preservation

Historic preservation is commonly used to protect old buildings and neighborhoods from deterioration. In 1981, the City of Milwaukee established a historic preservation commission to develop and maintain a local register of places with historical importance to the area. The commission also reviews all applications for historic status as well as any requests for exterior alterations. As such, there are numerous rules and restrictions that are imposed on property owners once it has been declared a historic site. Thus, while historic designation can serve to internalize the externalities in neighborhoods with historic buildings, it also imposes costs on homeowners who wish to make improvements to their homes. This paper uses a hedonic model to estimate the impact of historic preservation on the sale price of a single family home in the Milwaukee area. Preliminary results show that the impact of historic preservation is positive when it is significant, with the average impact at 26.6%. However, there was significant variation between districts, with the impact significantly positive in 13 of 22 districts used in the sample. Specifically, the positive impact ranged between 11% and 65%, holding other factors constant. None of the 22 districts had a negative and significant impact. An evaluation of spillover effects reveal that just over one third of them displayed positive and significant spillover effects, whereas 21% had negative and significant spillover effects. The remainder were insignificant. An important question is what factors influence this variability in historic preservation effects. The eventual goal of this research is to extend our preliminary analysis to two stages using a recently developed method that employs spatial econometric methods to solve the unique identification problems inherent in hedonic models (Carruthers and Clark, forthcoming in Journal of Regional Science). This will permit us to determine the specific factors that influence these premiums. While the spatial estimates presented in this preliminary work do not permit a two-stage model, we did explore whether implicit prices appear to be correlated with the household income and racial makeup of the neighborhoods in which they are located. The findings show little evidence that the implicit values of historic districts are correlated, but the implicit price associated with historic district spillovers was positively correlated with both neighborhood measures

Climate Ready Estuaries - COAST in Action: 2012 Projects from Maine and New Hampshire

In summer 2011 the US EPA’s Climate Ready Estuaries program awarded funds to the Casco Bay Estuary Partnership (CBEP) in Portland, Maine, and the Piscataqua Region Estuaries Partnership (PREP) in coastal New Hampshire, to further develop and use COAST (COastal Adaptation to Sea level rise Tool) in their sea level rise adaptation planning processes. The New England Environmental Finance Center worked with municipal staff, elected officials, and other stakeholders to select specific locations, vulnerable assets, and adaptation actions to model using COAST. The EFC then collected the appropriate base data layers, ran the COAST simulations, and provided visual, numeric, and presentation-based products in support of the planning processes underway in both locations. These products helped galvanize support for the adaptation planning efforts. Through facilitated meetings they also led to stakeholders identifying specific action steps and begin to determine how to implement them

Geographically Referenced Data for Social Science

An estimated 80% of all information has a spatial reference. Information about households as well as environmental data can be linked to precise locations in the real world. This offers benefits for combining different datasets via the spatial location and, furthermore, spatial indicators such as distance and accessibility can be included in analyses and models. HSpatial patterns of real-world social phenomena can be identified and described and possible interrelationships between datasets can be studied. Michael F. GOODCHILD, a Professor of Geography at the University of California, Santa Barbara and principal investigator at the Center for Spatially Integrated Social Science (CSISS), summarizes the growing significance of space, spatiality, location, and place in social science research as follows: "(...) for many social scientists, location is just another attribute in a table and not a very important one at that. After all, the processes that lead to social deprivation, crime, or family dysfunction are more or less the same everywhere, and, in the minds of social scientists, many other variables, such as education, unemployment, or age, are far more interesting as explanatory factors of social phenomena than geographic location. Geographers have been almost alone among social scientists in their concern for space; to economists, sociologists, political scientists, demographers, and anthropologists, space has been a minor issue and one that these disciplines have often been happy to leave to geographers. But that situation is changing, and many social scientists have begun to talk about a "spatial turn," a new interest in location, and a new "spatial social science" that crosses the traditional boundaries between disciplines. Interest is rising in GIS (Geographic Information Systems) and in what GIS makes possible: mapping, spatial analysis, and spatial modelling. At the same time, new tools are becoming available that give GIS users access to some of the big ideas of social science."

The Demand for Historic Preservation

Historic preservation is commonly used to protect old buildings and neighborhoods from deterioration. In 1981, the City of Milwaukee established a historic preservation commission to develop and maintain a local register of places with historical importance to the area. The commission also reviews all applications for historic status as well as any requests for exterior alterations. As such, there are numerous rules and restrictions that are imposed on property owners once it has been declared a historic site. Thus, while historic designation can serve to internalize the externalities in neighborhoods with historic buildings, it also imposes costs on homeowners who wish to make improvements to their homes. This paper uses a hedonic model to estimate the impact of historic preservation on the sale price of a single family home in the Milwaukee area. Preliminary results show that the impact of historic preservation is positive when it is significant, with the average impact at 26.6%. However, there was significant variation between districts, with the impact significantly positive in 13 of 22 districts used in the sample. Specifically, the positive impact ranged between 11% and 65%, holding other factors constant. None of the 22 districts had a negative and significant impact. An evaluation of spillover effects reveal that just over one third of them displayed positive and signficant spillover effects, whereas 21% had negative and significant spillover effects. The remainder were insignificant. An important question is what factors influence this variability in historic preservation effects. The eventual goal of this research is to extend our preliminary analysis to two stages using a recently developed method that employs spatial econometric methods to solve the unique identification problems inherent in hedonic models (Carruthers and Clark, forthcoming in Journal of Regional Science). This will permit us to determine the specific factors that influence these premiums. While the spatial estimates presented in this preliminary work do not permit a two-stage model, we did explore whether implicit prices appear to be correlated with the household income and racial makeup of the neighborhoods in which they are located. The findings show little evidence that the implicit values of historic districts are correlated, but the implicit price associated with historic district spillovers was positively correlated with both neighborhood measures.Hedonic housing model, historic preservation district, Milwaukee

Value of Trail Access on Home Purchases

We use hedonic analysis of home sales data from the Twin Cities Metropolitan Area to estimate the effects of access of different types of trails on home value. Our model includes proximity to three distinct types bicycle facilities, controlling for local fixed effects and open space characteristics. Using interaction terms detect different preferences between city and suburban homebuyers. Regression results show that off-street bicycle trails situated alongside busy streets are negatively associated with home sale prices in both the city and suburbs. Proximity to off-street bicycle trails away from trafficked streets in the city are positively associated with home sale prices, with no significant result in the suburbs. On-street bicycle lanes have no effect in the city and are a disamenity in the suburbs. The following policy issues are relevant from this research. First, type of trail matters. On-street trails and road-side trails may not be as appreciated as many city planners or policy officials think. Second, city residents have different preferences than suburban residents. Third and as suspected, larger and more pressing factors likely influencing residential location decisions. The finding also suggest that urban planners and advocates need to be aware of the consequences of providing for bicycle facilities, as the change in welfare is not necessarily positive for all homeowners.