Evaluating the Risk of Chinese Housing Markets: What We Know and What We Need to Know
Notice bibliographique
Résumé
Real estate is an important driver of the Chinese economy, which itself is vital for global growth.However, data limitations make it challenging to evaluate competing claims about the state of Chinese housing markets.This paper brings new data and analysis to the study of supply and demand conditions in nearly three dozen major cities.We first document the most accurate measures of land values, construction costs, and overall house prices.We then create and investigate a number of supply and demand metrics to see if price growth reasonably can be interpreted as reflecting local market fundamentals.Key results include the following:(1) Real house price growth has been high, averaging 10% per annum since 2004.However, there is substantial heterogeneity across markets, ranging from 3% (Jinan) to 20% (Beijing).House price growth is driven by rising land values, not by construction costs.Real land values have risen by over15% per annum on average.In Beijing, the increase has been by a remarkable 27.5% per year (or by 1,036%) since 2004.(2) There is variation about the strong positive trend in house price and land value growth.Land values fell by nearly one-third at the beginning of the global financial crisis, but more than fully recovered amidst the 2009-2010 Chinese stimulus.More recent growth has been much more modest, with some markets beginning to decline.Quantities of land sales by local governments to private residential developers have dropped sharply over the past two years.The most recent data show transactions volumes down by half or more.This should lead to a reduced supply of new housing units in coming years.(3) Market-level analysis of short-and longer-run changes in supply-demand balances finds important variation across markets.In the major East region markets of Beijing, Hangzhou, Shanghai and Shenzhen which have experienced very high rates of real price growth, we estimate that the growth in households demanding housing units has outpaced new construction since the turn of the century.However, there are a dozen large markets, primarily in the interior of the country, in which new housing production has outpaced household growth by at least 30% and another eight in which it did so by at least 10%.Regression results show that a one standard deviation increase in local market housing inventory is associated with a 0.45 standard deviation lower rate of real house price growth the following year.(4) There are no official data on residential vacancy rates in China, but some researchers have reported very high figures (17%+).We develop a new series at the provincial level which yields a much lower vacancy rate on average, but it has been rising-from 5% in 2009 to 7% in 2013.(5) The risk of housing even in markets such as Beijing which show no evidence of oversupply, is best evidenced by price-to-rent ratios.They are well above 50 in the capital city.Poterba's (1984)user cost model suggests these levels can be justified only if owners have sufficiently high expectations of future capital gains.Even a modest one percentage point drop in expected appreciation (or increase in interest rates) would result in a drop in prices of about one-third, absent an offsetting increase in rents.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,005 | 0,014 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».