Talk on the Block: Using public forum sentiment to predict housing prices

This paper develops a textual analysis methodology to quantify sentiment on public market forums to predict outcomes in the real estate market. This paper draws inspiration from Soo (2018) which quantified sentiment through real estate news media. We believe that analyzing public forums allows us to understand public sentiment in its most unedited, casual form; whereas real estate news media is limited to perspectives and interpretations of an editor. Antweiler and Frank (2004) showed that public forums are significant when predicting stock market outcomes, lending validity to our text source. Our methodology includes identifying a relevant dictionary of positive and negative words, scraping BiggerPockets real estate forums, running a textual sentiment analysis, and finally regressing against fundamental housing market indicators in 34 large metropolitan statistical areas (MSAs) to assess sentiment’s predictability on home prices. Our regression results suggest that sentiment significance varies more in the short-run with public forum text than it does with news media because news media is “markedto-market daily. ” Marking-to-market is the practice of valuing securities, or portfolios of securities, at their current market value, as opposed to a book value. Because news media is updated every day, and sometimes more than once a day, we find that it is capturing current market home values much more quickly than forum sentiment. Additionally, we conclude that discussion on real estate public forums can predict housing prices in the long-run, suggesting that the users are engaging in conversation that is targeting long-term investments and trying to make sense of the potential future value of a home that they are considering buying and/or selling. Talk on the Block An, Hill, Sammons, Toshniwal | 3 Introduction With the recent outbreak of COVID-19, financial markets have been hit hard and lack the consumer confidence to recover from such unforeseen unemployment and production shocks. Despite all the efforts to recover from the “Great Recession” of 2008 by regulators, credit rating agencies, and investment banks, which have corrected for the malpractice and misuse of collateralized debt obligations (CDOs) and mortgagebacked securities (MBS), this new virus outbreak poses a serious threat to the current housing market on the uncertain path to recovery. Regardless of the outbreak, the underlying assets of these financial instruments are lagging in recovery. Home values have not regained their value in a majority of MSAs, which is evident in-home market value listings being well below their proper tax-appraised value ratio of 33.33% on popular real estate sites, such as Zillow. During the 20th Century, median home values appreciated at exponential rates, averaging a 6.2% increase YoY from 1968-2004. Home values will likely never appreciate as such again do to the lack of space for new construction in major cities and surrounding suburban areas. As supply continues to run dry, demand will rise to new heights and the real estate market will need to adjust. Across many industries, a popular form of gauging demand is through the use of public forums. Zillow operates its own public forum, on which homeowners and home-hunters alike can share opinions about certain markets, trends in their neighborhoods, home valuations, and desired locations. Talk on the Block An, Hill, Sammons, Toshniwal | 4 Popular opinion would suggest that the most important aspect regarding real estate and home-buying is location. We believe that determining market temperature, that is whether certain markets are “hot” or “cold” for sellers and buyers, would help real estate companies, developers, and homebuyers find markets in which they can all benefit and equilibrate their respective housing markets. Additionally, in looking at text analysis to determine real estate market temperatures and trends, our group finds there are multitudes of data aggregation sites, such as newspaper articles and market indices, that track housing market sentiment. One such example is the monthlyreported University of Michigan Consumer Sentiment Index, which is regarded as a key temperature check on overall consumer confidence in the market. What the research does not capture is the chatter that occurs without a survey or the information that cannot be collected in a Wall Street Journal article. Thus, we believe that the best way to understand market temperature and bring home values back to the equilibrium of pre-2008 is to develop a predictive model based on public forums where raw emotions and sentiment are being shared every day. In this vein, our marginal impact over Soo's 2018 paper "Quantifying Sentiment with New Media across Local Housing Markets," will be to demonstrate that beyond news media, a better predictive model for housing market temperature and home values is one that incorporates daily chatter on the individual consumer level; that is, unedited public discussion boards and forums may provide better housing market temperatures in local markets. Talk on the Block An, Hill, Sammons, Toshniwal | 5 Literature & Dictionary Review In “Quantifying Sentiment with New Media across Local Housing Markets,” the author argues that direct impact on the housing prices is hard to ascertain since the degree of the price variation differs per area, thus requiring cross-sectional variables. After inspecting local news of 34 different cities in the U.S with textual analysis, the paper concludes that media sentiment is predominantly limited to local sources since housing prices are dictated by how each localized market is responding to home values and consumer confidence. On top of local media reflecting quantitative prediction for home prices, she also explains that these sentiment indexes can account for a wider range of variation, including post-housing bubble recovery with a time-lag. Soo's dictionary is based on the Harvard IV-4 Psychological Dictionary, a widely used resource for this type of analysis, but is improved by the addition of varying tenses and inflections for each word, both positive and negative. For example, the dictionary includes “Blooming” and “Bloomed”, as a variation of “Bloom. ” Soo modifies this dictionary to fit her textual input, financial news articles, and thus makes it a valuable resource in the undertaking of our research. Soo has generously provided us with this improved dictionary which serves as the foundation of our sentiment analysis. Talk on the Block An, Hill, Sammons, Toshniwal | 6 Data Acquisition & Preparation BiggerPockets Forum Data To perform a sentiment analysis on the impact of forum discussion on housing prices in the largest metropolitan statistical areas (MSAs), our methodology will mimic that of Soo in her paper, "Quantifying Sentiment with News Media across Local Housing Markets" (2018). As stated in our introduction, our goal is to provide marginal impact by taking what we hypothesize to be a more "raw" form of sentiment from buyers and sellers directly, as opposed to an edited and filtered newspaper article(s), and demonstrate predictability from this raw sentiment. The forum for this text scrape is BiggerPockets, an online platform founded in 2004 which aims at providing a real estate investment community for local real estate markets. BiggerPockets boasts nearly 5 million forum posts and 800 new daily active users, there is a plethora of entries to be used for analysis. Our analysis will focus on the "Buying & Selling Real Estate Discussion" forum, which contains over 500 thousand unique posts and 17 thousand unique discussions. This community continues to grow and focuses on general questions about buying and selling real estate and homeownership. Some questions are general and have threads from people all over the country, as well as marketspecific discussions. We believe that the frequency with which people interact on BiggerPockets, as well as its growing reputation amongst real estate investors, makes it one that will allow us to provide the marginal impact of unedited consumer sentiment. Talk on the Block An, Hill, Sammons, Toshniwal | 7 Dictionary & Sentiment Score Development We initially considered extending Soo’s dictionary to include ideograms, emojis or emoticons, and casual vernacular, but we’ve decided to adopt her dictionary without modification. From cursory reviews of the forum, we observe only limited use of casual language likely due to the site’s professional subject matter. As such, we do not believe that adding these elements will significantly affect our results. Also, by utilizing Soo’s previously applied dictionary rather than a custom one, we allow our results to be more closely compared to the existing body of sentiment analysis research. We begin our data preparation process by first filtering the raw data (courtesy of BiggerPockets) by location, removing any posts not included in our list of target MSAs. This step alone reduces our dataset from 500 thousand posts to approximately 280 thousand posts. Centering this more manageable dataset, we apply a series of textual treatments. The body text of each post is cleaned of HTML formatting before being tokenized or broken into a list of successive words. Next, we clean these tokenized lists of 'stop words' or words, like articles, that do not typically convey relevant information. The extensive stop word dictionary we apply is provided by the NLTK Project in its Natural Language Toolkit module. Finally, a negation rule is used to handle the reversing effect of negations on sentiment-carrying words. The final dataset contains more than 8.1 million individual words at an average of around 60 words per post. This preparation approximates Soo's process as described in her paper. Talk on the Block An, Hill, Sammons, Toshniwal | 8 After applying these treatments, it is possible to generate the sentiment index. Using an extended form of Soo's dictionary