DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal (CIKM 2023)
Official code of the paper DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal. Paper link: http://arxiv.org/abs/2309.00855.
DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. And, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks.
python DoRA.py {building_type} {num_shot}
{building_type} = 'building', 'apartment', 'house'
{num_shot} = 1, 5, ...
| Feature | Feature Type (#class) | Example |
|---|---|---|
| City Name | Category (21) | Taipei city |
| Town Name | Category (350) | Ren’ai township |
| Parking spot | Category (2) | True/False (If the house includes a parking spot.) |
| Studio | Binary | True/False (If the area of the estate is smaller than 8 square meters.) |
| Details building type | Category (5) | Residential building (11 floors and above), Mansion (10 floors and below) |
| Main Purpose | Category (1622) | Electromechanical equipment space |
| Building materials | Category (220) | Rebar, Wood |
| Management organization | Binary | True/False |
| Type of parking space | Category (3) | Flat parking spot, Automated parking spot |
| Elevator | Binary | True/False |
| First-floor index | Binary | True/False (If the house is located on the first floor.) |
| Shop index | Binary | True/False (If the house is for shop use.) |
| Housing type | Category (3) | Building, Apartment, House |
| Village name | Category (4650) | Zhongshan village |
| Land use | Category (19) | Residential zone, Forestry land, Mining land |
| Land Use Designation | Category (16) | Type A building land, Class B building site |
| Land transfer area | Numerical | 30 |
| Building transfer area | Numerical | 50 |
| Number of bedrooms | Numerical | 2 |
| Number of living rooms | Numerical | 1 |
| Number of bathroom | Numerical | 3 |
| Number of total rooms | Numerical | 5 |
| Parking area | Numerical | 5 |
| Main building area | Numerical | 100 |
| Ancillary building area | Numerical | 10 |
| Balcony area | Numerical | 5 |
| House age | Numerical | 10 years |
| Number of land transaction | Numerical | 1 |
| Number of building transaction | Numerical | 1 |
| Number of parking space transactions | Numerical | 2 |
| Building area without parking area | Numerical | 45 |
| Single floor area | Numerical | 20 |
| Floor area ratio (FAR) | Numerical | 10 (Derived by dividing the total area of the building by the total area of the parcel.) |
| Estate floor | Numerical | 5 |
| Total floor | Numerical | 10 |
| Latitude | Numerical | Horizontal lines that measure distance north or south of the equator |
| Longitude | Numerical | Vertical lines that measure east or west of the meridian in Greenwich, England. |
| Building coverage ratio | Numerical | 9 |
| Park count flat | Numerical | 0 |
Generally, real estate with many YIMBY facilities often has a higher price since it implies the quality of living and the degree of transportation convenience. On the contrary, real estate with many NIMBY facilities may be likely to have a lower price since it indicates there may be some pollutant issues that cause a negative impact on living
| Feature | Feature Type (#class) | Example |
|---|---|---|
| YIMBY_10 | Numerical | 2 |
| YIMBY_50 | Numerical | 3 |
| YIMBY_100 | Numerical | 3 |
| YIMBY_250 | Numerical | 6 |
| YIMBY_500 | Numerical | 7 |
| YIMBY_1000 | Numerical | 13 |
| YIMBY_5000 | Numerical | 28 |
| YIMBY_10000 | Numerical | 52 |
| NIMBY_10 | Numerical | 0 |
| NIMBY_50 | Numerical | 0 |
| NIMBY_100 | Numerical | 0 |
| NIMBY_250 | Numerical | 1 |
| NIMBY_500 | Numerical | 1 |
| NIMBY_1000 | Numerical | 2 |
| NIMBY_5000 | Numerical | 3 |
| NIMBY_10000 | Numerical | 6 |
| Feature | Feature Type (#class) | Example |
|---|---|---|
| Land area per town | Numerical | 23.13 (km2) |
| Population density per town | Numerical | 23835 (#people/km2) |
| House price index per quarter | Numerical | 110 |
| Unemployment rate per quarter | Numerical | 5% |
| Economic growth rate per quarter | Numerical | 3% |
| Lending rate per quarter | Numerical | 1.9% |
| Land transaction count per quarter | Numerical | 163796 |
| Average land price index per quarter | Numerical | 101 |
| Steel price index per quarter | Numerical | 1071 |