An empirical investigation of framing effects in negotiations: A study of single-family home sales

2008 ◽  
Vol 25 (5) ◽  
pp. 465-484 ◽  
Author(s):  
Carl L. Witte ◽  
Marko Grünhagen ◽  
James W. Gentry
Keyword(s):  
Empirical Investigation ◽  
Framing Effects ◽  
Single Family ◽  
Home Sales ◽  
Family Home

scholarly journals Productivity in the US Housing Industry: Total Factor and Public Firms

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Michael S. Puddicombe
Keyword(s):  
Primary Source ◽  
Basic Question ◽  
Wealth Accumulation ◽  
Single Family ◽  
Time Series Regression ◽  
Data Set ◽  
Public Firms ◽  
The Us ◽  
Home Sales ◽  
Housing Industry

Productivity is widely recognized as one of the main contributors to increased economic and societal wellbeing. Unfortunately, productivity has been extremely difficult to operationalize in a repeatable context in the construction sector. The result is a lack of consensus on the basic question of whether there has been improvement or decline in the productivity of the sector. This study focuses on productivity in the housing industry. Productivity is especially important in this industry, as in addition to providing shelter, the housing market is the primary source of wealth accumulation in the US. An individual’s ability to enter this market will be a function of affordability which will be effected by the productivity of the industry. The combination of academic and societal impacts suggests that there is a need to address a fundamental question: what is the status of productivity in the housing industry. In order to address this question a data base was compiled from the 10-Ks of the largest, long lived, US companies in the single family housing industry. The result is a panel data set that consists of information on 11 firms over a 15-year period. These 11 firms were responsible for approximately 25% of all new home sales in any given year. The data set was analysed with random effects GLS time series regression. The results indicate that, at best, the housing industry has seen negligible total productivity growth.

scholarly journals Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

2020 ◽  
Vol 17 (17) ◽  
pp. 6359 ◽  
Author(s):  
Quynh C. Nguyen ◽  
Yuru Huang ◽  
Abhinav Kumar ◽  
Haoshu Duan ◽  
Jessica M. Keralis ◽  
...  
Keyword(s):  
Computer Vision ◽  
Built Environment ◽  
Local Area ◽  
Physical Disorder ◽  
Single Family ◽  
Google Street View ◽  
Environment Effects ◽  
Street View ◽  
Sociodemographic Disparities ◽  
Family Home

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

Value of Transit as Reflected in U.S. Single-Family Home Premiums

2016 ◽  
Vol 2543 (1) ◽  
pp. 108-115 ◽  
Author(s):  
Shima Hamidi ◽  
Katherine Kittrell ◽  
Reid Ewing
Keyword(s):  
Single Family ◽  
Family Home

scholarly journals Landscape Quality and the Price of Single Family Houses: Further Evidence from Home Sales in Greenville, South Carolina

1999 ◽  
Vol 17 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Mark S. Henry
Keyword(s):  
South Carolina ◽  
House Prices ◽  
House Price ◽  
Price Premium ◽  
Single Family ◽  
Home Sales ◽  
Landscape Quality

Abstract In this study, the contribution of the quality of landscaping to house prices was estimated for a sample of 218 home sales in Greenville, SC, from 1996 to 1997. The estimates were made using regressions of house price on house characteristics, location and landscape quality. The results obtained in this research were similar to an earlier study of Greenville home sales, from 1991 to 1993. For homes with the same square footage and other house characteristics, selling prices were 6% to 7% higher if landscaping quality was judged excellent rather than good. The price premium obtained by upgrading landscaping from average to good was approximately 4% to 5%.

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