Data-Driven Office Rental Price Prediction: An Empirical Study of Deep Learning vs. Gradient Boosting

The ability to predict office rental prices is very important in urban planning, investment strategy and regulation of the market. Nevertheless, it is still difficult to value commercial real estate because of extreme data heterogeneity. Predictive models should simultaneously be able to handle structured attributes that are rigid (e.g., floor area and level) and unstructured textual information (e.g., location tags and amenity descriptions), and there is no trivial way to reconcile the two.

To address this, we built a new multi-modal dataset that focuses on commercial office listings in Hangzhou, China. We directly combine web-scraped transaction data with rich geospatial Point of Interest (POI) measures obtained through mapping APIs.

In the recent past, the scholarly world has been overwhelmingly in favor of multi-modal deep learning models to process such heterogeneous data. However, our empirical results lead towards a different direction. We prove that Gradient Boosting Decision Trees (GBDT), namely CatBoost, not only significantly outperforms traditional statistical approaches in the commercial real estate field, but also decisively defeats complex deep learning baselines, including multi-modal fusion models based on encoders such as BERT.

In addition to raw predictive performance, we have also opened the black box of rental valuation by using feature importance analysis and found that specific spatial features are much more important in driving prices than generic textual embeddings. In the end, this paper suggests a practical workflow of real estate analytics, which demonstrates that in tabular-dominant cases, strict feature engineering with effective tree models can be used to outperform over-parameterized neural networks.