Stay Ahead of Manhattan's Evolving Rental Market with an R Shiny App
Overview
The Manhattan rental market is constantly shifting, and the pandemic exacerbated these fluctuations. With this in mind, I have developed a Shiny app to empower landlords and brokers with a comprehensive understanding of the rental market, enabling them to optimize pricing for new tenants and lease renewals. By offering in-depth data on crucial factors, such as median price, the number of available listings, and the percentage of listings featuring price reductions, users can make well-informed decisions when determining rental rates for their apartments.
Data
The app utilizes a monthly updated dataset sourced from StreetEasy, a prominent apartment rental website in New York City. To maintain data integrity, missing values were addressed and the data was stored in long format R dataframes. Instead of merging the files, they were kept separate to preserve essential metadata, simplify filtering and subsetting, and ensure compatibility with R Shiny visualization packages. This approach, while requiring more storage and additional packages, allowed for improved data management and flexibility throughout the analysis process.
Using the App
The side panel enables users to filter data based on the start date, end date, and apartment size to match their specific needs. The three tabs (Median Asking Rent, Percent of Listings Discounted, Amount of Listings) share a consistent layout featuring three information boxes, a line graph, and a neighborhood selection option. The information boxes display data at the beginning and the end of the selected range and also show the percentage of change during that period. Users can select multiple neighborhoods to accommodate apartments located on neighborhood bordersThe displayed data will reflect the median values of the selected areas.
For instance, consider a landlord with a one-bedroom apartment in the East Village whose tenants are ending their lease after two years. The first tab reveals that the median asking rent has increased by 46% from $2,400 two years ago. This suggests that the landlord might be able to increase the rent by more than 40% compared to the previous time the apartment was listed.
However, the second tab offers a different perspective. It shows that 29% of one-bedroom listings in the East Village have been discounted from their original asking price, a greater drop than we saw during the height of the pandemic two years ago. This indicates that rental prices may have risen too quickly, resulting in a large number of discounted listings as landlords seek a more competitive price point.
The final tab highlights the number of comparable listings on the market. While there were 912 listings two years ago, the current number has dropped to 276 – a 70% decrease. Analyzing all three data points reveals that landlords were able to fill vacant units due to an influx of tenants returning to the city, even as rental prices increased. However, there now appears to be a shortage of new tenants willing or able to afford the higher rates, leading landlords to offer discounts to meet the available demand. Consequently, landlords who attempt to increase the rent by 46% may find it difficult to find new tenants and may be compelled to offer discounts to attract them.
Final Thoughts
This app serves as a valuable tool for landlords and brokers in the data-driven age, delivering crucial insights into Manhattan's dynamic rental market. Equipped with this information, users can make informed decisions when pricing their apartments to optimize both profitability and competitiveness.
View my Shiny app or my Github to explore the code.
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