A Natural Language Processing Model for House Price Forecasting

Technical Summary:

Real estate agents frequently write property descriptions to convey its unique features and history, reduce market friction, and emphasize any competitive advantages, thereby increasing the likelihood of a sale.This algorithm is able to analyze the text based on mathematical theories and neural network deep learning, an AI model that imitates the human brain, to obtain distributed representations of words, sentences, and paragraphs. This technology preserves the semantic meanings of words within the context of the paragraph and detects more detailed nuances than sentiment analysis methods based on words’ positive/negative polarity. Additionally, the algorithm is able to learn the individual agent’s writing style and understand abbreviations/typos, as MLS systems impose a 250-words limit on the description length. This technology takes the arrangement order of sentences and paragraphs into consideration, as the relative position of a sentence in a description often implies the importance of the corresponding feature it describes.