Housing Media Analysis


Say "goodbye" to housing news overwhelm!


Using cognitive computing powered by IBM Watson®, Housing Tides Media Analysis interprets the sentiment from over 500 U.S. housing news articles/month.

Housing Tides Media Analysis Quick Facts

  • Every month, there is an abundance of housing market news.  IBM Watson® natural language processing technology allows Housing Tides to correctly understand and synthesize large volumes of housing media, including translating language into “sentiment” or the author’s explicit meaning.  We then aggregate that information into a consolidated, interactive form that you can easily access and understand.
    • Sentiment scores range from -1 to +1; articles expressing the most negative sentiment earn a -1 and the articles expressing the most positive sentiment earn a +1.

  • Filters allow you to tailor media results to your region, time period, source, or keyword.

  • Sentiment analysis of major media sources helps predict economic outcomes.
    • Economic outcomes are the aggregation of the decisions of individuals with limited knowledge obtained through major media channels… don’t underestimate the effects that media coverage has on housing markets & home builder equity returns!

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Example Housing Tides Media Analysis

Media Analysis for all sources, Nov 2019 - Jan 2020

Gain a Competitive Advantage in Your Market!

Discover the synergy of the Housing Tides Index, Media Analysis, Housing Tides Permit Forecasts™.  When you combine the insight from all three, you gain a high-resolution, comprehensive view of the US housing market. 

Dive Deeper

IBM Watson natural language processing technology allows Housing Tides to correctly understand and synthesize large volumes of housing media, including translating language into “sentiment” or the author’s explicit meaning, and aggregate that information in a consolidated form that users can easily access and understand.

Watson Natural Language Classifiers provide a reliable filter, ensuring that only articles about relevant aspects of the housing markets are included (for example, leaving out an article about a robbery at a multi-family apartment building and including an article about increased multi-family apartment development).

The result is a gauge of the overall media sentiment surrounding the housing market and home construction industry. Sentiment scores are determined for each news article or blog via the IBM Watson Alchemy Sentiment Analysis API, using natural language processing to assign a sentiment value to each piece of text. Sentiment scores range from -1 to +1; articles expressing the most negative sentiment earn a -1 and the articles expressing the most positive sentiment earn a +1. Most articles fall within a narrow band as the majority of news expresses mixed positive and negative sentiment.