The rebound in the housing market since the financial crisis has been bullish since 2011. This has caused rental rates to rise across the country. Such trends are particularly consipicuous in the major cities such as New York and San Francisco. In second tier cities, such as Cleveland or even Chicago, rents have not been on the rise to the same extent.
One of the most interesting trends is looking at the New York City housing market in comparison to San Jose and San Francisco. According to Zillow 1-bedroom rental data, New York City rental prices were the highest in the country just last year. Since then, the rental prices of San Jose and San Francisco, CA have surpassed those of NYC. Could the tech boom in Silicon Valley be leading to a housing bubble there? Maybe not in the near term, but if the 'bubble' were to pop, it would likely be caused by "a lot of these [tech] companies failing and not needing real estate," according to Hessam Nadji, managing director at real estate investment services firm Marcus & Millichap. And if the tech companies don't need real estate, it is likely they won't need as many employees either. Who knows whether this will actually occur in the coming years but the excessive rental price escalation in the San Francisco Bay area is unprecedented as compared to any other citiy during the recovery.
Have a look at the data and see for yourself! Utilizing Zillow Research data, we have displayed 1-bedroom rental rate trends across the US in the visualizations below.
Source: Zillow Research Rental Data
The animated map below depicts the change in the median 1-bedroom rental prices of major cities across the United States. The red colors indicate a drop in the rental prices for cities and the green dots indicate a rise in the overall rental price. A stark contrast of a trend is observed as the map, starting from early 2010, which shows a fall in the rental prices across all the cities in the United States, and as time goes by, most cities exhibit a significant increase in the rental prices once the housing recovery began in 2012.
Source: Zillow Research Metrics
One of the hardest questions that every household faces at some point is the grand 'rent or buy' question. The answer, of course, is that 'it depends.' Many factors, such as the expected duration of occupancy, mortgage rates, interest rates and growth rates are all taken into consideration to determine whether one should rent or purchase a house. However, the most significant factor would be the price of a house relative to its rent. While the suspicion is that they are highly correlated, outliers would indicate that one may be far better off with a home purchase in one area, whereas it may be better to rent in another city. An interesting finding is that in September 2015, while San Francisco and San Jose's rents are both extremely expensive, San Jose's house sales prices are significantly cheaper, making it a good neighborhood to consider a buy option. Fiddling with the dropdown menu, it can also be seen that New York has increasingly become unattractive for househorders due to the significant rise of sales price and a relatively modest increase in its rental prices. To make this trend more clear, we produced a rough estimate by fitting a line to the data to more easily show the outliers. On average, the sales price is ~12x the yearly rent.
Source: Zillow Research Home Prices
Methods and Experiences
Even before building the visualizations, we realized our historical data from Zillow did not have latitude and longitude coordinates for the major US cities. Of course, we needed this data to plot the cities as points on the map. We solved this by using the Google API with each city name to query for the coordinates.
We have already constructed maps, line charts, and scatter plots in class. Improving the functionality and adding features to the charts is what took most of our time. For example, adding the dropdown menu within some of the visualizations, allowing the user to choose exactly which city or timeframe to investigate further, was a struggle. It first needed to be populated with the various city names or dates. We also created a unique identifier for each city (different from the city name). Attaching a class to the points and lines in the charts equal to this identifier allowed us to highlight those points and lines when the dropdown selection was changed. A smaller struggle was setting the number of ticks on the x-axis in the first line chart. Since the labels are considered categorical, we can't simply set the number of ticks. Instead we had to use a workaround using a filter statement.
Another struggle was getting the tooltip to work properly in an iframe. If the tooltip was displayed outside of the svg (e.g. for a city on the right side of the map), normally it would just run over the side of the svg. Since the iframe is like a separate page within the current page, we could not have the tooltip display outside the iframe. Our temporary solution in some of the visualizations was to simply to give the tooltip the same position (at the top of the visualization) no matter what was selected.