高盛-中國房地產：高線城市將引領全國房價回升（摘要）

中國房地產下行周期已進入第四個年頭，但目前尚無明確的觸底回升跡象。由于房價企穩對于消費者信心恢復和提振市場情緒來說至關重要，所以高盛利用國家統計局發布的70城二手房價格數據，將城市分成四個不同的集群來進行聚類分析，其目的是為了證實各城市房地產市場間的差異是否顯著，進而推出哪一類城市的房價會率先完成觸底。

集群1主要由一線城市和強二線城市組成。

集群2主要由普通二線城市組成。

集群3主要由普通三線城市組成。

集群4主要由人口外流的弱三線城市組成。

以下是正文：

原文：The ongoing downturn in the property sector has now extended into its third consecutive year. In the September Politburo meeting, Chinese policymakers pledged to “stem the decline and facilitate the stabilization” of the property market, which we interpret as stabilizing existing home prices.1 While secondary home prices – which better reflect market conditions than primary home prices that tend to be heavily regulated by local governments – have recently started to show narrower price declines in top-tier cities, a clear nationwide stabilization remains uncertain (圖表 1, left chart). Given the strong correlation between house prices and consumer confidence, we believe stabilizing house prices remains crucial for supporting household consumption and broader market sentiment (圖表 1, right chart).

Against this backdrop, we look at the National Bureau of Statistics (NBS) 70-city existing property prices data to examine regional patterns, investigate the drivers behind the house price changes, and draw lessons for potential future house price movements.

譯文：房地產行業的低迷現已持續三年，9月底的政治局會議明確了房地產市場止跌回穩的目標，高盛將其解讀為穩定現有房價，二手房價格比受到地方政府嚴格監管的新房價格更能反映市場狀況，最近一線城市的二手房價跌幅有所收窄，但全國范圍內的房價仍難言企穩（圖1，左圖）。鑒于房價與消費者信心之間的強相關性，我們認為穩定房價對于支持家庭消費和提振市場情緒來說至關重要（圖1，右圖）。

在此背景下，我們研究了國家統計局的70城房價數據，以分區域研究模式來調查房價變化背后的驅動因素，并為未來的房價變動提供判斷依據。

圖表1：二手房價格尚未觸底，房價穩定對經濟復蘇仍然至關重要

左圖為二手房價格預測，數據包括統計局70城房價（深藍），中原地產6城房價（紅色），諸葛找房100城房價（灰色），貝殼25城房價（淡藍），國信達房產價格（綠色）。右圖為房價與消費者信心的走勢對比，藍線表示中國消費者信心，紅線表示預期房價上漲的百分比。

分析：從左圖可以看出，幾乎所有房價指數在2024年都是下跌的，包括926新政后的第四季度房價依然下跌，所以24年第四季度只能算是一個不是很成功的反彈，市場主要還是以價換量為主。再看24到25年的房價預測，諸葛找房、統計局、中原房產分別預測25年到底房價繼續下跌6%、7.5%、12.5%，可見高盛對于現有政策強度下的25年房價比較悲觀。

從右圖可以看出，消費者信心和預期房價呈很強的趨同性，這也解釋了目前這么多促銷費政策下去但效果卻差強人意的原因，和目前國家不遺余力要穩住房地產市場的原因，因為目前房地產市場不但拖累了整體經濟，還是通縮治理的核心所在。

原文：For background, there are two main sources of property indices for major cities: official data from the NBS and data from private providers such as Centaline and Zhuge. The NBS 70-city secondary home price index, covering mostly large and medium-sized cities, relies on data from real estate agencies, field investigations, and local housing authority registries. Given the shorter time series and limited city coverage of many private data sources, we focus on the NBS house price index in the analysis below. However, it is not without limitations. For example, local governments face pressure to stabilize property prices in both upturns and downturns, which often leads to data that is overly smoothed, understating actual price fluctuations2.

We first group the property price indices in the NBS 70-city sample using K-means clustering analysis, which divides the data into clusters based on the similarity of their property price trends. The K-means algorithm assigns each city to the nearest centroid (the average of data points in a cluster) and then iteratively adjusts the centroids to minimize the variance within each cluster. To determine the optimal number of clusters, we use the elbow method. This involves plotting the within-cluster variance for different numbers of clusters (K) and choosing the point where adding more clusters no longer improves the result. Using this approach3, we identify four distinctive city clusters based on their house price trends from 2011 to the present (圖表 2).

譯文：主要城市的房地產數據包括兩種：一種是來自統計局的官方數據，另一種是來自中原房產等第三方數據。統計局70城的二手房價指數主要覆蓋了大中型城市，數據源于房地產機構、實地調查、當地住房管理局登記處。鑒于統計局數據城市覆蓋范圍大且包含第三方數據源，我們在下面的分析中重點關注統計局房價指數。但統計局的數據也并非沒有缺陷，比如地方政府在經濟上行和不景氣時都面臨穩定房地產價格的壓力，這通常會導致數據過于平滑，從而低估了實際價格的波動。

這段是統計學建模的描述，不用過于關注，所以這里只做簡單翻譯：我們首先使用K-means聚類分析對統計局70城樣本中的房價指數進行分組，根據其房價趨勢的相似性將城市劃分為多個集群。使用這種方法，我們根據2011年至今的房價趨勢確定了四個獨特的城市群（圖2）。

圖表 2:中國住房市場中四個獨特的城市群

分析：以上是根據統計局70城房價數據，利用K值聚類分析做出來的四個組，圖中紅線表示該組的房價均值，在這里高盛只是介紹所使用的統計學模型，對于普通投資者來說不需要關注，只需要參考其結果即可。

另外高盛所說的70城數據由于穩房價壓力而過于平緩，說的已經很含蓄了，各位可以自行體會，一般來說我的處理方法是將統計局的數據×2左右以得到接近真實的波動數據。

原文：First, Cluster 1 includes mostly tier-1 and large tier-2 cities, where house prices have shown larger price appreciation and greater resilience (e.g., limited price decline in the 2014-15 downturn). The majority of the 70 cities – 77% on a population-weighted basis – fall into Clusters 2 and 3, which consist primarily of medium-sized tier-2 and tier-3 cities (see Appendix for more detailed city composition and geographic distribution, 圖表 8 and 圖表 9). Cluster 4 consists of cities such as Jinzhou, Mudanjiang (both in the northeast and with population outflows), and Wenzhou which had a housing bust in the early 2010s.

譯文：首先，集群1主要包括一線和強二線城市，這些城市的房價表現出更大的升值空間和更強的彈性（例如在2014到2015年經濟低迷期間房價下跌有限）。70城中的大多數（按人口加權計算為77%）屬于集群2和集群3，主要由普通二線和普通三線城市組成（更詳細的城市構成和地理分布見圖8和圖9）。集群4由錦州、牡丹江（均位于東北部且人口外流）和溫州等城市組成，溫州入選的原因是因為在2010年代初經歷了房地產泡沫。

分析：以上是高盛對于以統計局70城的分組列表和地理分布圖。

原文：Second, as shown in 圖表 3, most cities reached their peak house prices before December 2022, with cities that have stronger fundamentals peaking later. Around 57% of the 70 cities peaked between January 2020 and December 2022, while only 6% peaked after December 2022. On average, Cluster 1 cities peaked the latest, around mid-2022, likely supported by stronger demand fundamentals and more stringent supply restrictions. This is followed by Clusters 2 and 3, which peaked around late 2020 to mid-2021. Cluster 4 cities peaked the earliest, with their house prices continuing to decline despite a brief pick up during the 2015-18 shantytown redevelopment program.

譯文：其次，如圖3所示，大多數城市在2022年12月之前達到了房價峰值，而基本面較強城市的房價達峰較晚。70城市中約有57%的城市在2020年1月至2022年12月期間房價達到峰值，有6%的城市在2022年12月之后房價達到峰值。平均而言，集群1的城市最晚在2022年年中左右達到峰值，這可能是由于更強勁的需求基本面和更嚴格的限購措施被放開；集群2和集群3在2020年底至2021年中達到峰值；集群4的城市最早達到峰值，盡管房價在2015到2018年棚改期間短暫回升，但最終繼續下跌。

圖表 3：2020年至2022年期間，超過一半的城市達到了房價峰值

分析：以上是四個集群的城市房價達峰的比例，高盛發現的規律是越是高線的城市房價達峰越晚，越是低線的城市房價達峰越早，這背后的原因如上面所解釋的，就是高線城市經濟和人口基本面更好，而且限購等措施緩慢放開導致需求源源不斷的被釋放，所以房價達峰的時間會比較晚，這也符合基本的邏輯和經濟規律。

原文：Third, while the recent decline in house prices is similar across clusters – ranging from 15% to 20% since their peaks – the cumulative gains since 2011 differ markedly (圖表 4 ). Cluster 1 cities have seen house prices appreciate by 75% over the period, Cluster 2 cities have experienced only 20% cumulative increase, and the latest declines have wiped out all prior gains in Cluster 3. Cluster 4 cities stand out as the only group to experience a net price decline over the last decade. These trends highlight that stronger fundamentals not only delay the timing of house price peaks but also preserve some of the earlier price gains during a nationwide housing downturn.

譯文：第三，雖然近期各集群房價的下跌幅度相似，都是自峰值下跌15%到20%不等，但自2011年以來的累計漲幅卻截然不同（圖4），集群1城市的房價累計上漲了75%，集群2城市的房價累計漲幅僅為20%，近兩年的下跌抹去了集群3城市之前的所有漲幅，而集群4城市是過去十年中唯一出現價格凈下降的組。這些趨勢說明一二線城市強勁的基本面不僅推遲了房價達峰的時間，而且在全國房地產低迷期間的抗跌性也會更強。

圖表 4：房價達峰后各集群的房價下跌幅度相似，但自2011年以來的累積漲幅截然不同

分析：上圖是四個集群從房價達到峰值后的跌幅（深藍）和房價從2011年至今的累計漲幅（淡藍），可以看出集群1、2、3的房價達到峰值后的跌幅類似，集群4房價達到峰值后跌幅比其他三個集群都要大，這是因為集群4的城市經濟和人口基本面均不如前三個集群所致。另外從2011年后累計漲幅的角度上看，越是基本面好的高線城市累計漲幅越大，基本面最差的集群4城市從2011年至今的漲幅為負。

高盛想表達的意思是，城市的選擇對于房產增值來說至關重要，高線城市由于基本面長期保持強勁而表現為強者恒強，其房價在牛市時漲得多熊市時跌的少，是購房的首選城市。

原文：圖表 5 suggests that population flows4 are important for house prices. The left panel shows that every 10% increase in population growth from 2010 to 2020 corresponds to approximately 3% rise in house prices in the 2010s. For example, cities like Chengdu and Zhengzhou, which experienced population inflows of around 45% over the decade, saw house price increases of around 50%. In contrast, some northeastern cities such as Jilin, which faced a 20% population decline, recorded a 20% rise in house prices. The 65% disparity in population growth between these two groups of cities accounts for roughly 30% variation in house price performance during the period. The right panel suggests that cities with strong population inflows in the 2010s also experienced milder price declines in the recent housing downturn.

譯文：圖表5表明人口流動對房價來說很重要。左圖顯示，從2010年到2020年，城市人口每增加10%帶來的房價漲幅約為3%。例如成都和鄭州等城市在過去十年中累積了約45%的人口流入，對應的房價上漲了約50%，作為對照的吉林等一些東北城市人口下降20%，對應的房價上漲了20%，這兩組城市之間65%的人口增長差距導致房價表現的差異約為30%。右圖表明，在2010年到2020年間人口流入較多的城市在最近的房地產低迷期中房價下跌也比較溫和。

圖表 5： 人口增長與房價有很強的相關性

分析：上圖是高盛根據2010年到2020年歷史數據做出的房價與人口關系對比圖，得出的結果就是城市人口每增加10%可以貢獻約3%的房價漲幅，這個數據可以直接當作結論來用。

原文：Another driver for the recent house price declines could be the elevated housing supply. Longer inventory months – measured as the sellable gross floor area divided by the 12-month rolling gross floor area sold – tend to exert downward pressure on prices. As shown in 圖表 6 (left chart), inventory months have risen sharply in lower-tier cities since 2021. Among the seven cities classified in Clusters 2 and 3 that also have inventory months data, there is significant variation in average inventory months since 2023, with Dalian the highest (49 months) and Hangzhou the lowest (10 months) (圖表 6, right chart).

譯文：近期房價下跌的另一個驅動因素是住房供應量的增加，較長的庫存去化周期（以可售總建筑面積除以12個月滾動銷售總建筑面積來衡量）會對房價產生下行壓力。如圖6（左圖）所示，2021年以來低線城市的庫存去化周期快速上升，在集群2和集群3的7個城市中，自2023年以來的平均庫存去化周期存在顯著差異，其中大連最高（庫存去化周期49個月），杭州最低（庫存去化周期10個月）（圖6，右圖）。

圖表 6：低線城市房產庫存增加

分析：左上圖可以清楚看到三線城市在2023年后的庫存增加幅度遠大于一二線城市，這也是三線城市房價表現遠不如一二線城市的重要原因之一。右上圖是集群2和集群3里面挑出來的7個城市的庫存去化周期對比，大連的去化周期高達49個月，而杭州去化周期只有10個月，這就解釋了杭州的房價為什么遠比大連的房價堅挺的原因。

原文：To summarize the two drivers of house price dynamics, we estimate a panel regression model to link annual house price growth with 1) changes in inventory months5 and 2) population growth between 2010 and 2020. We also control for the 1-year lag of house price growth as well as time fixed effects to account for other uncontrolled factors that may drive house prices. The analysis was conducted using two panels, one focusing on cities in Clusters 2 and 3, and another including all available cities in the dataset for increased statistical power. The results are largely consistent across the two panels (圖表7）.

譯文：為了總結房價波動的兩個驅動因素，我們建立了一個面板回歸模型將房價增長與庫存周期（月數）的變化和2010年至2020年的人口增長聯系起來。我們還通過統計學手段去除了可能影響房價的其他不受控因素。分析是用兩個面板進行，一個面板側重于集群2和集群3中的城市，另一個面板包括所有城市，分析的結論是兩個面板的結果基本一致（圖表7）。

圖表 7：在回歸模型中，人口變化和住房庫存都推動了房價的波動

分析：上圖可以不用看懂，只需要明白高盛通過回歸模型的分析證明了一件事，就是人口和房產庫存這兩個因素對房價確實有顯著的影響，換句話說，在其他因素不變的前提下，人口增加和房產庫存減少的城市房價會顯著上漲，這個結果也可以直接當做結論來用。

原文：We find that a one-month increase in housing inventory supply leads to around 20bp decline in house price growth. Meanwhile, a 10% increase in population between 2010 and 2020 results in an average annual house price increase of 0.3%, accumulating to 3% over a decade – consistent with our earlier scatter plot analysis. Combining these estimates, average population increased 23% in our Clusters 2 and 3 sample over the last decade, which accounts for roughly 28% of the total house price appreciation during this period. Moreover, housing inventory has risen by about 17 months since the price peak in 2020-22, contributing to around 18% of the subsequent price decline.

Furthermore, we find that the persistence of house price growth, as measured by the one-year lagged house price growth, is less pronounced (0.2%) compared to the US (around 0.4%). This implies that house prices are less sticky in China, potentially reflecting the larger role of government policies in the economy.

While demographics fundamentals and housing inventory are important drivers of house prices, they don’t explain all of the house price movements, and other variables not captured by our model also play an important role. For example, previous academic literature illustrated the importance of household income in driving local house prices.

Taken together, our analysis suggests that both structural population shifts and cyclical fluctuations in housing inventory supply are important drivers of house prices in China. Therefore, we expect top-tier cities like Shanghai and Shenzhen, bolstered by stronger migration inflows, lower inventory levels and the recent easing of housing purchase restrictions, may find a price bottom sooner than the rest of the nation – consistent with our property team’s view. In contrast, while inventory destocking may provide some relief, the implementation challenges and still limited government support suggest that lower-tier cities may continue to face pressure on housing prices, further exacerbated by migration outflows.

譯文：我們發現住房庫存的去化周期每增加一個月會導致房價下降約0.2%。2010年至2020年期間人口增長10%，期間房價年均上漲0.3%，十年內累計上漲3%，與我們之前的散點圖分析結果一致。利用以上的估算，在過去十年中集群2和集群3城市的平均人口增長了23%，貢獻了同期房價總升值的28%。自2020到2022年房價見頂以來，住房庫存已經上漲了17個月，貢獻了房價總貶值的18%。

另外我們發現中國的房價粘性比美國更低，這反映了中國政府的政策在中國經濟中發揮的作用比美國政府的政策在美國經濟中發揮的作用更大。

雖然人口基本面和住房庫存去化周期是房價的重要驅動因素，但它們并不能解釋所有的房價變動，回歸模型中不包含的其他變量也起著至關重要的作用，比如家庭收入在推動當地房價方面的重要性。

綜上所述，我們的分析表明，結構性人口轉移和住房庫存去化周期是中國房價波動的重要驅動因素。因此在更強勁的人口流入、更低的房產庫存水平、以及最近被放寬的限購等政策的推動下，一線城市的房價會比其他城市的房價更早完成觸底。相比之下，低線城市會繼續面臨房價壓力，而人口外流會進一步加劇這種房價壓力。

分析：高盛總結了庫存去化周期和人口因素對于城市房價的影響，結論就是住房庫存去化周期每減少一個月貢獻的房價漲幅約為0.2%，城市人口每增加1%貢獻的房價漲幅約為0.3%。另外我國的政策對本國經濟的影響大于美國的政策對本國經濟的影響，換句話說就是同樣一個政策，在中國的作用比在美國的大，所以政策是改變我國經濟走勢非常重要的因素之一。

需要注意的是，模型只對人口和房產庫存對房價的影響做了有效性驗證，而并沒有包括其他影響房價的關鍵因素，比如最重要的家庭收入因素，這在邏輯上也很直觀，家庭沒有錢就沒法買房。