Accuracy of Households' Dwelling Valuations, Housing Demand and Mortgage Decisions: Israeli Case.

Housing policy, as well as academic research, are increasingly concerned with the role of bias in subjective dwelling valuations as a proximate measure of households' house price expectations and their relationship with housing demand. This paper contributes to this area of study by exploring the possibility of simultaneous relationships between households' price expectations and incentive to maximise the size of housing services demanded also accounting for the supply side factors and regional perspective. The empirical estimation takes the form of a system of a two simultaneous equations model applying two stage least squares estimation technique. Cross sectional estimations utilise data extracted from the Israeli Longitudinal Panel Survey (LPS) data. Applying the best available proxy for households' price expectations, calculated as the ratio between subjective dwelling valuations (LPS) and the estimated market value of the same properties, research has identified the interrelated factors that simultaneously influence householders' price expectations and housing demand. Results offer conceptual and empirical advantages, highlighting the imperfect nature of the housing market, as reflected by the inseparability of bias in subjective valuations and housing decisions.

Where do Property Rights Matter More? Explaining the Variation in Demand for Property Titles across Cities in Mexico.

This paper examines the variation in the value of property rights to housing in Mexico, focusing specifically on differences between urban housing markets. Roughly 30% of owner-occupied houses in Mexico do not have a proper deed. Houses with no deed are estimated to be five percent less valuable than otherwise similar houses with a full deed, yet this premium varies widely across cities. I match data from the 2012 and 2014 National Survey of Household Incomes and Expenditures to different sources of city-level data in order to examine hypotheses explaining this variation in a multilevel regression framework. I find that deeds are valued more in cities with more highly educated residents, more political competition, and more voting. Measures of local economic activity, degree of informality, and the regulatory bureaucracy are not associated with higher value to full property rights. Additionally, I find that more educated households value deeds more, and having a deed is more valuable for larger houses in neighborhoods with less vacancy and higher infrastructure quality. Based on these results, I suggest funds to subsidize titling should be redirected to places where titles are worth more. More broadly, I suggest policymakers reconsider framing property-titling programs as poverty alleviation. Low-income households would benefit more from subsidies for improvements to housing and residential infrastructure. At the same time, the federal government should further push to reduce the costs of transferring property.

Hotspots of wetland loss to impervious surfaces in the conterminous United States.

In this study, a first wall-to-wall comparison between the National Wetlands Inventory (NWI) and the National Land Cover Database (NLCD) was conducted across the entire conterminous United States (CONUS) to evaluate U.S. wetland loss conditions. Annually, around 26 km2 of wetlands are lost to impervious surfaces across the CONUS. Spatially, wetland loss is not evenly distributed, with 90 % of losses occurring in only 9 % of the land area, forming hotspots around expanding urban regions such as Houston, Jacksonville, and Naples. Over the past few decades, Florida experienced the highest wetland loss (5.73 km2/year) among all states, while Houston had the most wetland loss (2.54 km2/year) among all metropolitan regions. Stepwise multiple regression models identified population growth and its associated demand for new housing as the major drivers for wetland loss. Wetland loss per population increase is the highest (>15 m2/person) in most metropolitan regions around the East Coast and Gulf of Mexico. Unfortunately, current wetland loss hotspots will likely suffer further losses in future decades due to projected population growth, with Houston, Cape Coral, and Miami metropolitan regions having the greatest projected wetland loss of 89.15 km2, 34.35 km2, and 28.20 km2, respectively. This study has identified wetland loss hotspots and their drivers across the U.S. that were not possible in previous sample-based studies. The findings are critical in wetland management and protection across the U.S.

A dataset of housing market and self-attitudes towards housing location choices in Alexandria, Egypt.

A survey, of sample size 224, is designed to include the different related-factors to housing location choice, such as; socioeconomic factors, housing characteristics, travel behavior, current self-selection factors, housing demand and future location preferences. It comprises 16 questions, categorized into three different sections; socioeconomic (5 Questions), current dwelling unit characteristics (7 Questions), and housing demand characteristics (4 Questions). The first part, socioeconomic, covers the basic information about the respondent, such as; age, gender, marital status, employment, and car ownership. While the second part, current dwelling unit characteristics, covers different aspect concerning the residential unit typology, financial aspects, and travel behavior of the respondent. It includes the tenure types of the residential unit, estimation of the unit price (in the case of ownership or renting), housing typologies, the main reason for choosing the unit, in case of working, the modes of travel to work, and time to reach it, residential mobility in the last decade, and the ownership of any other residential units. The last part, housing demand characteristics, covers the size of the demand for a residential unit, preference in living in a certain area and the reason to choose it, and the preference of residential unit׳s tenure. This survey is a representative sample for the population in Alexandria, Egypt. The data in this article is represented in: How do people select their residential locations in Egypt? The case of Alexandria; JCIT1757.