DETERMINANTS OF RETURNS TO HOMEOWNERSHIP: COUNTY LEVEL ANALYSIS FROM 1999 TO 2009
Bibliographic record
Abstract
INTRODUCTION Homeownership has always been an integral part of 'American Dream.' Homeownership has been attributed to building stronger communities. Most people dream of owning their own home and people take pride in becoming homeowners. At macro level, housing industry has always been a major contributor to economic growth. Its impact on overall U.S. economy was made quite demonstrably evident over last decade or so. The housing sector was major driver that helped us pull out of dot com crash of 2000 and 2001. It also was primary tipping point for crash of 2008. Many economists have argued that a robust, sustaining recovery will not take place without a housing sector that is once again growing and creating jobs. With Case-Shiller index recently showing first signs of life since housing crash (it ended second quarter of 2012 with positive annual growth for first time since summer, 2010), there appear to be hopeful signs on horizon. (1) Over last few years, causes of housing crisis (especially at national level) have been discussed and debated extensively in popular press. However, there is a paucity of empirical research that examines returns to homeownership at local level over this 'unusual' period. With that in mind, we analyze returns to homeownership at county level for 1999-2009 period. While there is quite a significant variation of returns across 3,133 counties examined, most returns are positive and quite significant. We further examine determinants of these returns using a variety of socioeconomic and demographic factors. We find geographic location, population density, percent of renters in county, and availability of vacant houses for sale to be factors that significantly affect returns. We also examine mortgage lending practices, but don't find subprime lending to be a factor that affects returns during this period. The next section of paper reviews existing literature and provides motivation for our paper. The data sources and methodology used are described next. Findings and a discussion of our results follow. The final section contains our conclusions and recommendations for further research. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Given importance of housing sector--both to individuals and to broad economy--much research has focused on risk and return in housing market. Articles in popular press have analyzed rent versus buy decision with a focus on 'breakeven horizon.' Using data crunched by Zillow, CNNMoney recently reported results for ten major cities in U.S. (2) The article defines breakeven horizon as, the length of time a new homebuyer would have to own their home before it would make better financial sense to buy, rather than rent? In Boston, New York, Los Angeles, and San Francisco, homes were expensive enough that it would generally make sense to rent--in spite of rents being high as well. In other 6 cities (Chicago, Dallas, Philadelphia, Washington D.C., Miami and Atlanta) decision leaned towards buying since breakeven horizons were well under 3 years. While a number of studies have utilized nationwide data; quite a few others have focused on regional market data. Using data for four large metropolitan areas, Case and Shiller (1990) demonstrate that price changes are a function of factors such as construction costs and changes in adult population. Rose (2006) analyzes investment value of home ownership. The author calculates returns based on cash outflows needed to purchase a home. She incorporates tax savings, differences in cash flows for buying versus renting and assumes a 5% annual home price appreciation. She concludes that home investment may be one of best long-term investments. Cannon, Miller, and Pandher (2006) conduct a cross-sectional risk-return analysis that covers metropolitan housing market. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".