Describe the house and I will tell you the price: House price prediction with textual description data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract House price prediction is an important problem that could benefit home buyers and sellers. Traditional models for house price prediction use numerical attributes such as the number of rooms but disregard the house description text. The recent developments in text processing suggest these can be valuable attributes, which motivated us to use house descriptions. This paper focuses on the house asking/advertising price and studies the impact of using house description texts to predict the final house price. To achieve this, we collected a large and diverse set of attributes on house postings, including the house advertising price. Then, we compare the performance of three scenarios: using only the house description, only numeric attributes, or both. We processed the description text through three word embedding techniques: TF-IDF, Word2Vec, and BERT. Four regression algorithms are trained using only textual data, non-textual data, or both. Our results show that by using exclusively the description data with Word2Vec and a Deep Learning model, we can achieve good performance. However, the best overall performance is obtained when using both textual and non-textual features. An $R^2$ of 0.7904 is achieved by the deep learning model using only description data on the testing data. This clearly indicates that using the house description text alone is a strong predictor for the house price. However, when observing the RMSE on the test data, the best model was gradient boosting using both numeric and description data. Overall, we observe that combining the textual and non-textual features improves the learned model and provides performance benefits when compared against using only one of the feature types. We also provide a freely available application for house price prediction, which is solely based on a house text description and uses our final developed model with Word2Vec and Deep Learning to predict the house price.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it