## 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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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.