04 Real Estate Investment Analysis
Interfaces implemented in this chapter are listed below; uncovered indicators are noted under “To be added” at the end.
Real estate development investment
Interface: macro_china_real_estate_invest
Source URL: https://data.stats.gov.cn/dg/website/page.html#/pc/national/monthData
Description: NBS monthly series “Real estate development investment”. Includes cumulative completed investment and cumulative YoY for real estate development overall and for residential, office, and commercial business buildings.
Limits: Returns all monthly data within the specified time range in a single call
Input
| Name | Type | Description |
|---|---|---|
| start | str | Start month YYYYMM; default 200001 |
| end | str | End month YYYYMM; default 203612 |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | e.g. 2026-06 |
| Month name | object | e.g. Jun 2026 |
| Real estate investment-Cumulative | float64 | Unit: 100 million yuan |
| Real estate investment-Cumulative YoY | float64 | Unit: % |
| Residential investment-Cumulative | float64 | Unit: 100 million yuan |
| Residential investment-Cumulative YoY | float64 | Unit: % |
| Office investment-Cumulative | float64 | Unit: 100 million yuan |
| Office investment-Cumulative YoY | float64 | Unit: % |
| Commercial business building investment-Cumulative | float64 | Unit: 100 million yuan |
| Commercial business building investment-Cumulative YoY | float64 | Unit: % |
Example
import meshare as ms
df = ms.macro_china_real_estate_invest()
print(df.tail())
df = ms.macro_china_real_estate_invest(start="202001", end="202606")
print(df[["月份", "房地产投资-累计值", "房地产投资-累计同比"]].tail())
Commodity housing sales area and sales value
Interface: macro_china_commodity_house_sales
Source URL: https://data.stats.gov.cn/dg/website/page.html#/pc/national/monthData
Description: NBS monthly series “Newly built commodity housing sales area” and “Newly built commodity housing sales value”. Merged return of cumulative values and cumulative YoY. Before August 2005 the series uses actual-sales scope; thereafter it includes both forward and completed housing.
Limits: Returns all monthly data within the specified time range in a single call
Input
| Name | Type | Description |
|---|---|---|
| start | str | Start month YYYYMM; default 200001 |
| end | str | End month YYYYMM; default 203612 |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | e.g. 2026-06 |
| Month name | object | e.g. Jun 2026 |
| Commodity housing sales area-Cumulative | float64 | Unit: 10,000 sqm |
| Commodity housing sales area-Cumulative YoY | float64 | Unit: % |
| Commodity housing sales value-Cumulative | float64 | Unit: 100 million yuan |
| Commodity housing sales value-Cumulative YoY | float64 | Unit: % |
Example
import meshare as ms
df = ms.macro_china_commodity_house_sales()
print(df.tail())
df = ms.macro_china_commodity_house_sales(start="202001", end="202606")
print(df[["月份", "商品房销售面积-累计值", "商品房销售额-累计值"]].tail())
Housing starts, under construction, and completions
Interface: macro_china_real_estate_area
Source URL: https://data.stats.gov.cn/dg/website/page.html#/pc/national/monthData
Description: NBS monthly series “Real estate floor area under construction and completed”. Includes cumulative values and cumulative YoY for floor area under construction, newly started, and completed.
Limits: Returns all monthly data within the specified time range in a single call
Input
| Name | Type | Description |
|---|---|---|
| start | str | Start month YYYYMM; default 200001 |
| end | str | End month YYYYMM; default 203612 |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | e.g. 2026-06 |
| Month name | object | e.g. Jun 2026 |
| Floor area under construction-Cumulative | float64 | Unit: 10,000 sqm |
| Floor area under construction-Cumulative YoY | float64 | Unit: % |
| Newly started floor area-Cumulative | float64 | Unit: 10,000 sqm |
| Newly started floor area-Cumulative YoY | float64 | Unit: % |
| Completed floor area-Cumulative | float64 | Unit: 10,000 sqm |
| Completed floor area-Cumulative YoY | float64 | Unit: % |
Example
import meshare as ms
df = ms.macro_china_real_estate_area()
print(df.tail())
df = ms.macro_china_real_estate_area(start="202001", end="202606")
print(df[["月份", "新开工面积-累计值", "施工面积-累计值", "竣工面积-累计值"]].tail())
Sources of funds for real estate development
Interface: macro_china_real_estate_fund
Source URL: https://data.stats.gov.cn/dg/website/page.html#/pc/national/monthData
Description: NBS monthly series “Actual funds in place for real estate development investment”. Includes cumulative values and cumulative YoY for total funds this year, domestic loans, foreign funds, and self-raised funds.
Limits: Returns all monthly data within the specified time range in a single call
Input
| Name | Type | Description |
|---|---|---|
| start | str | Start month YYYYMM; default 200001 |
| end | str | End month YYYYMM; default 203612 |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | e.g. 2026-06 |
| Month name | object | e.g. Jun 2026 |
| Total funds-Cumulative | float64 | Unit: 100 million yuan |
| Total funds-Cumulative YoY | float64 | Unit: % |
| Domestic loans-Cumulative | float64 | Unit: 100 million yuan |
| Domestic loans-Cumulative YoY | float64 | Unit: % |
| Foreign funds-Cumulative | float64 | Unit: 100 million yuan |
| Foreign funds-Cumulative YoY | float64 | Unit: % |
| Self-raised funds-Cumulative | float64 | Unit: 100 million yuan |
| Self-raised funds-Cumulative YoY | float64 | Unit: % |
Example
import meshare as ms
df = ms.macro_china_real_estate_fund()
print(df.tail())
df = ms.macro_china_real_estate_fund(start="202001", end="202606")
print(df[["月份", "资金来源小计-累计值", "国内贷款-累计值", "自筹资金-累计值"]].tail())
National Real Estate Climate Index
Interface: macro_china_real_estate
Source URL: http://data.eastmoney.com/cjsj/hyzs_list_EMM00121987.html
Description: NBS — National Real Estate Climate Index
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Date | object | - |
| Latest | float64 | - |
| Change % | float64 | - |
| 3M change % | float64 | - |
| 6M change % | float64 | - |
| 1Y change % | float64 | - |
| 2Y change % | float64 | - |
| 3Y change % | float64 | - |
Example
import meshare as ms
macro_china_real_estate_df = ms.macro_china_real_estate()
print(macro_china_real_estate_df)
Sample data
Date Latest Change % ... 1Y change % 2Y change % 3Y change %
0 1998-01-01 98.60 NaN ... NaN NaN NaN
1 1998-02-01 98.99 0.395538 ... NaN NaN NaN
2 1998-03-01 99.05 0.060612 ... NaN NaN NaN
3 1998-04-01 100.81 1.776880 ... NaN NaN NaN
4 1998-05-01 101.68 0.863010 ... NaN NaN NaN
.. ... ... ... ... ... ... ...
280 2022-03-01 96.66 -0.278552 ... -4.618117 -1.598290 -4.306504
281 2022-04-01 95.89 -0.796607 ... -5.349916 -3.023867 -5.190825
282 2022-05-01 95.60 -0.302430 ... -5.533597 -3.774534 -5.402731
283 2022-06-01 95.40 -0.209205 ... -5.619311 -4.456685 -5.628648
284 2022-07-01 95.26 -0.146751 ... -5.683168 -4.825657 -5.757816
New house price index
Interface: macro_china_new_house_price
Source URL: http://data.eastmoney.com/cjsj/newhouse.html
Description: China new house price index, monthly; data from 201101 to present
Limits: Returns all historical data for the specified cities in a single call
Input
| Name | Type | Description |
|---|---|---|
| city_first | str | e.g. city_first="北京" (Beijing); city list on the source site (Chinese labels) |
| city_second | str | e.g. city_second="上海" (Shanghai); city list on the source site (Chinese labels) |
Output
| Name | Type | Description |
|---|---|---|
| Date | object | Date |
| City | object | - |
| New commodity residential price index-MoM | float64 | - |
| New commodity residential price index-YoY | float64 | - |
| New commodity residential price index-Fixed base | float64 | - |
| Second-hand residential price index-MoM | float64 | - |
| Second-hand residential price index-YoY | float64 | - |
| Second-hand residential price index-Fixed base | float64 | - |
Example
import meshare as ms
macro_china_new_house_price_df = ms.macro_china_new_house_price(city_first="北京", city_second="上海")
print(macro_china_new_house_price_df)
Sample data
Date City New commodity residential price index-YoY ... Second-hand residential price index-YoY Second-hand residential price index-MoM Second-hand residential price index-Fixed base
0 2011-01-01 Shanghai 101.8 ... 101.7 100.5 100.6
1 2011-01-01 Beijing 109.1 ... 102.6 100.3 101.2
2 2011-02-01 Beijing 108.4 ... 102.9 100.4 101.5
3 2011-02-01 Shanghai 102.8 ... 102.0 100.4 101.0
4 2011-03-01 Beijing 106.2 ... 101.9 99.9 101.4
.. ... .. ... ... ... ... ...
327 2024-08-01 Beijing 96.4 ... 91.5 99.0 NaN
328 2024-09-01 Shanghai 104.9 ... 92.4 98.8 NaN
329 2024-09-01 Beijing 95.4 ... 89.7 98.7 NaN
330 2024-10-01 Shanghai 105.0 ... 93.3 100.2 NaN
331 2024-10-01 Beijing 95.1 ... 91.6 101.0 NaN
[332 rows x 8 columns]
To be added
Land transactions, deposits & advance receipts / personal mortgage and other funding-source details, and property-type sales / starts-completions breakdowns are not yet wrapped.