16 research outputs found

    Who Cares About School Quality?; The Role of School Quality in Household Preference, School District Choice, and Willingness to Pay

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    School quality is considered a key factor affecting homebuyers\u27 location choices and willingness to pay. Previously, many studies found that school quality plays a critical role in determining housing prices and location choice. School quality is positively capitalized into housing prices. Households are willing to pay for school quality, in particular, school outcomes such as test scores and performance index. However, there is a view that willingness to pay for school quality is different based on household demographics and socioeconomic status (SES). The purpose of this dissertation is to investigate heterogeneous preference for school quality, school district choice, and willingness to pay for school quality according to a household\u27s demographic background and SES, including the presence of school-age children, marital status, income, education, race/ethnicity, and occupation. This dissertation takes occupational variables into account in the model as a proxy for human capital. This dissertation was also developed to find whether or not a household\u27s preference for school quality leads to their school quality consumption regarding school district choice and willingness to pay. Two datasets were used: the 2006 homebuyer\u27s survey and the 2006 transacted housing sales in Cuyahoga County, Ohio. With the aggregated dataset, the national model also analyzes household demographics and school quality data aggregated by school district in 2,531 school districts in 14 states. Three models were used to test the groups of hypotheses for preference, school district choice, and willingness to pay: ANOTA, ordered logit, and the hedonic price model. The findings of this dissertation indicate household heterogeneous preferences for school district choices and willingness to pay for school quality. It also found a gap between preference for school quality and actual consumption of school quality. In particular, larger gaps appear in low-income, low-educated, and single-head households than in other households. These finding

    Containing a firestorm: adaptive policies needed to address changing foreclosure landscape

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    Like a wildfire leaving devastation in its path, the foreclosure crisis continues to wreak havoc on many families and communities throughout the Fourth District, especially in the largest urban areas. Only a year ago the primary reason for foreclosures centered on subprime mortgages. Today, the primary driver is unemployment, further widening the consumption arc of this blaze.Foreclosure

    Charity and Prosperity: The Economic Impact of Public Charities in Arkansas 2006-2010

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    In 2010, public charities in Arkansas had a total economic impact of 13,505,145,972.Arkansasnonprofitorganizationsemployedanestimated93,095individualsin2010,representingnearly7percent(6.813,505,145,972. Arkansas nonprofit organizations employed an estimated 93,095 individuals in 2010, representing nearly 7 percent (6.8%) of the state's available labor force. In addition to these impressive numbers, public charities in the state provide a host of services to Arkansans -- from educational opportunities to health care to housing, shelter, and food.Nonprofit organizations are legal entities formed to provide services and programs. These organizations typically engage in activities without financial profit, although these organizations may retain excess revenue. Nonprofit revenue in excess of cost are untaxed and may be saved for future use. This report describes the Arkansas nonprofit sector in terms of its activities, composition, employment levels, and employee earnings. Upon providing a portrait of nonprofit organizations, the report offers an assessment of the nonprofit sector's economic effect on the state economy.Data for this study are from the Urban Institute's National Center for Charitable Statistics (NCCS), and are comprised of IRS Form 990 and Form 990-EZ filings for all registered 501(c)(3) public charities in Arkansas with over 25,000 in total revenue per year. Data for calendar years 2006 through 2010 are analyzed for this study; data for 2011 and 2012 are not yet available.In examining only those organizations with more than 25,000inrevenue,thisstudyrepresentsapproximatelyone−thirdofallnonprofitsregisteredinArkansasasnodataareavailablefororganizationswithtotalrevenueunder25,000 in revenue, this study represents approximately one-third of all nonprofits registered in Arkansas as no data are available for organizations with total revenue under 25,000 (these organizations are not required to file annual reports to the IRS). These data include information only for public charities, which are guided by 501(c)(3) rules. In doing so, this report excludes information about private foundations, churches, social and fraternal organizations, or other groups considered tax-exempt under other sections of the tax code. Consequently, results presented in this report actually understate the true effects of the nonprofit sector for Arkansas. Therefore, when discussing results about nonprofits in Arkansas, this research is addressing the effect of service provided by public charities onl

    MODELING THE EFFECTS OF REFINERY EMISSIONS ON RESIDENTIAL PROPERTY VALUES

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    ABSTRACT This research studied the effects of refinery air pollution on house prices near Houston, Texas. The affected area was identified through AERMOD air modeling of past releases of sulfur dioxide, a proxy for respiratory risk. A total of 3,964 residential MLS sales from 2006-2011 were used to populate an OLS model, a spatial model, and a spatial model with an additional endogenous variable. Findings indicate that air pollution has a significant negative 6-8% loss on house prices. For one year, the negative effect is shown to generally diminish with distance up to about two miles from the refinery

    Urbanity, Financial Crisis and the Timing of Homebuying Decisions by Young Households

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    Large urban areas have a high appeal to young households, particularly highly educated ones, as they offer higher wages, more job opportunities and urban amenities. However, large urban areas are also characterized by higher rental rates and single-family home prices. We investigate the timing of homebuying decisions by young households (25 to 28 years) in large metropolitan (metro) areas compared to young households in other areas. Using the PSID database and Cox regression, we find that wealth, likelihood to move, being married, race and macro-economic variables affect the timing of homebuying decisions of young households irrespective of where they live. However, income is more important for the timing of homebuying decisions by young households in large metro areas than other areas and a college education is less important. Furthermore, large metro households are more likely to delay homeownership than households in other areas. In poor economic conditions such as the 2007 to 2009 financial crisis, mobility considerations are predominantly driving the timing of homebuying decisions by large metro households while income is most important for homebuying decisions of young households in other areas. Our results suggest that young homebuyers in large metro areas differ from those in other areas and that urbanity is important in understanding homebuyer behavior

    Does the Written Word Matter? The Role of Uncovering and Utilizing Information from Written Comments in Housing Ads

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    The hedonic price model is a popular method to estimate the implicit prices of observed attributes of a property. However, the inputs to the model are only numerically quantified information. This study quantifies the unstructured qualitative statements contained in the written descriptions from the Multiple Listing Service (MLS) data. These statements contain unstructured text describing the features and setting of the house, providing important but typically unused qualitative information. Our approach is unique in that we use the qualitative information to classify these words into eight groups that reflect previously unmeasured housing quality. The purpose of the study is to test whether these previously unmeasured attributes of the property have an impact on the selling price of the property and its time on the market. The dataset consists of 5,160 home sales in Ames, Iowa between the second quarter of 2003 and the second quarter of 2015. Our findings show that the role of unstructured qualitative text varies; some are redundant to the quantitative information already in the models and have no effect, while others, particularly those reflecting the quality of the structure, represent unique information and are important predictors in determining housing prices and the time on market
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