DETERMINANTS OF RETURNS TO HOMEOWNERSHIP: COUNTY LEVEL ANALYSIS FROM 1999 TO 2009
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».