https://github.com/nebyte/housevaluation
This AI model processes location-based property features (size, floor, amenities, etc.) to estimate real estate values. It includes scraping, training, and prediction capabilities.
https://github.com/nebyte/housevaluation
ai api estate finance idealista prediction prediction-model real-estate rl spain
Last synced: 3 months ago
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This AI model processes location-based property features (size, floor, amenities, etc.) to estimate real estate values. It includes scraping, training, and prediction capabilities.
- Host: GitHub
- URL: https://github.com/nebyte/housevaluation
- Owner: NEBYTE
- Created: 2025-02-12T11:51:22.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T16:17:50.000Z (4 months ago)
- Last Synced: 2025-02-12T17:28:19.572Z (4 months ago)
- Topics: ai, api, estate, finance, idealista, prediction, prediction-model, real-estate, rl, spain
- Language: Rust
- Homepage:
- Size: 153 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# House Valuation AI (v0.1.0-pre.alpha.1)
[](https://github.com/carlos-crypto)
[](https://www.rust-lang.org/)
[](https://choosealicense.com/licenses/agpl-3.0/)
> [!NOTE]
> This is a **machine learning-powered real estate valuation tool** built in Rust. It uses **Elastic Net Regression** to predict property prices based on **historical data** from real estate listings. **As of v0.1.0-pre.alpha.1, Spain + Idealista API is the only country available for scraping.**
This AI model processes **location-based property features** (size, floor, amenities, etc.) to estimate real estate values. It includes **scraping, training, and prediction capabilities**.
---
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Scrape Data](#scrape-data)
- [Train the Model](#train-the-model)
- [Predict Property Prices](#predict-property-prices)
- [Technical Overview](#technical-overview)
- [Dependencies](#dependencies)
- [License](#license)
---
## Features
> [!WARNING]
> **This model is experimental.** It is not a financial advisory tool and should not be used for critical investment decisions.
- **Property Price Estimation** - Uses **Elastic Net Regression** to predict real estate prices.
- **Automated Data Scraping** - Fetches property data from Idealista.
- **Machine Learning Training** - Builds a model using historical property listings.
- **Feature Extraction** - Uses **location, size, rooms, bathrooms, and more** as predictive factors.
- **Evaluation Metrics** - Computes **R² score** to assess model performance.
- **Prebuilt Database** - The package comes with 2500+ homes in spain with 13 data fields per house.
---
## In action
```shell
What do you want to do?: Predict Property Price
Enter the size (m²) of the property: 196
Enter the floor number (leave empty if not applicable): 6
Enter the latitude: 32
Enter the longitude: 0.43
Does the property have a lift? yes
Enter price per m² (if not known, leave empty): 3500
Number of bedrooms: 4
Number of bathrooms: 3
Does the property have a swimming pool? yes
Does the property have a garden? yes
Does the property have a garage? yes
Loaded model from output/cervo_model.bin
✅ Loaded existing trained model.
💰 Predicted price: €1122271.88
What do you want to do?:
> Scrape Data
Predict Property Price
Train Model
Exit
```
---
## Installation
### Prerequisites
- **Rust** (latest stable)
- **Cargo** package manager
- **CSV dataset** (automatically generated from scraper)
### Clone the Repository
```sh
git clone https://github.com/NEBYTE/HouseValuation.git
cd HouseValuation
```
### Build the Project
```sh
cargo build --release
```
---
## Usage
### Scrape Data
```sh
cargo run --release
```
This will **scrape Idealista real estate listings** and save them to `data/idealista_homes_spain.csv`.
---
### Train the Model
```sh
cargo run --release
```
The model will process the dataset and generate a **trained Elastic Net Regression model** saved to `output/cervo_model.bin`.
---
### Predict Property Prices
To estimate the price of a custom property:
```sh
cargo run --release -- predict
```
It will prompt the user for property details (size, rooms, location, etc.) and return a **predicted price**.
---
## Technical Overview
### **Machine Learning Model**
- **Algorithm**: Elastic Net Regression
- **Input Features**:
- **Geolocation** (latitude, longitude)
- **Size** (square meters)
- **Floor level**
- **Number of rooms & bathrooms**
- **Amenities** (pool, garden, garage, lift)
- **Evaluation Metric**: **R² Score**
### **Dataset Handling**
- **CSV Format**
- **Auto-generated via scraper**
- **Stores property attributes & prices**
### **Risk & Error Handling**
- **Missing values handling**
- **Data normalization**
- **Cross-validation (K=5)**
---
## Dependencies
```toml
[dependencies]
csv = "1.2"
ndarray = "0.15"
linfa = "0.7.0"
linfa-elasticnet = { version = "0.7.0", features = ["serde"] }
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
dotenv = "0.15.0"
dialoguer = "0.11.0"
```
---
## License
Distributed under the [GNU AGPLv3](https://choosealicense.com/licenses/agpl-3.0/) license.