Austin Apartment Deals: A Renter’s Cheatsheet

Kwelia provides price-transparency for the apartment-rental industry. By analyzing millions of data-points, Kwelia can uncover the best deals available for renters, intelligently price apartments for property owners, and predict upcoming real-estate trends for investors.

 

In our last posting, we reviewed Austin’s top apartment deals. Unfortunately (for renters) many of last week’s deals are no longer available, giving testament to Austin’s highly desired real estate market; In 2013, Austin showed above a 95% occupancy rate for apartments,  leaving little room for future renters. Fortunately, Kwelia is here to save the day with yet another iteration on this week’s top  apartment deals in the Austin area.

Sabina

If you’re a renter who prefers a quieter, more homely (the “cozy” definition, not the “unattractive” one) neighborhood, check out Sabina properties, located in North Austin’s Hancock area. Sabina offers a 2-bedroom, 2-bath, 1,080 square foot apartment with monthly payments of $1,950; Sabina throws in a free month’s rent, reducing the renters’ overall monthly payments to $1,787/month. As a result, Kwelia ranks the Sabina a cool 85/100.

Why this deal wins

Not only does Sabina offer a quiet, safe neighborhood but the apartment comes furnished with luxury granite countertops, stainless-steel appliances and in-unit washer & dryer. Access to an elevator makes moving-in easy to the newly renovated apartments. Additional value is derived from Sabina’s 1-month free promotion when you sign the lease.

 

Berkshire SoCo

If you’re not a fan of the north, head south and check out Berkshire SoCo. Berkshire is currently offering a 1,221 square foot, 2-bedroom, 2-bath luxury apartment for $1,694/month. Berkshire’s offering has been rated a 65/100 by Kwelia’s algorithms.

Why this deal wins

While Berkshire SoCo is not as highly ranked as Sabina, the property offers a huge advantage – a garage. With a car, a renter will still need to drive 16 minutes into town but will have the flexibility for weekend getaways and explore the beautiful, more rural parts of Texas.

 

Falcon Ridge

If you enjoy Berkshire SoCo’s location, you might want to also check out Falcon Ridge – a 2 minute drive further south. The additional 2-minute drive will yield a 1,120 square foot apartments with 2-bedroom and 2 bathrooms priced at $1,266 (after 1-free month lease signing). The deal offered at Falcon Ridge is almost too good to pass up, scoring a Kwelia rating of 74/100.

Why this deal wins

Not only does Falcon Ridge offer luxury apartments with elevators for easy move-in, but Falcon Ridge offers many outdoor activities including, but not limited to, access to Williamson Creek, a volleyball court, two swimming pools, jogging trains, and public BBQ grills – perfect for some fun in the sun!

Coming up next week…

Grab these apartments while you still can because, like our last posting, most apartments are only on the market for a week! None the less, the team at Kwelia will be your eyes-and-ears keeping you up-to-date on the best apartment deals within the area.


If you’d like to learn more about Kwelia, sign up for our mailing list or login to find other apartment deals throughout the area.

Apartment (Re)search: How Google Search Trends Provide Insights Into Rental Prices

At Kwelia, we’re big fans of looking for answers in data. While we often explore rental market trends, we’ve also explored other topics, such as the correlation between gas prices and rental prices, and performed Twitter sentiment analysis for a VP debate. Pulling in outside data sources such as gas prices can help to highlight interesting trends while also revealing connections between data. With that in mind, if you’re looking to cast your data in a different context, which outside data should you investigate and where can you find it? While the answer will certainly vary depending on what you are interested in, when it comes to data, few, if any, companies have more of it than Google. In fact, the billions of searches per day that Google uses to tailor advertisements to users can also act in the aggregate as a powerful tool to observe trends.

Google search data has been used for a diverse set of purposes from influenza epidemiology and estimating retail sales to finding movements in unemployment, inflation, automobile demand, and vacation destinations. Others have also found a correlation between Google searches for home real estate agencies and the Case-Shiller housing price index (incidentally, Robert Shiller, one of the creators of the index, was recently a co-recipient of the 2013 Nobel Memorial Prize in Economics).

Does search interest translate into rental demand?

Given how well search volume is able to detect changes in a plethora of “real-world” data, how well does search volume track changes in rental prices?  Put differently, do rent prices vary with more searches?  With fewer?

Using Google Trends, we obtained time-series data on searches for apartments in the San Francisco Bay Area, New York City, Philadelphia, and Atlanta. The search data contains relative search volume rather than absolute search volume.  That means, for example, that if we lookup the prevalence of the search term “smartphone” in San Francisco in September, rather than receiving results saying that “smartphone” was 5% of all SF searches in one week vs. 3% of SF searches in another week, the search interest is reported as numbers between 1 to 100. This is done by indexing the maximum frequency for the “smartphone” searches over the course of September at 100, and presenting all other data relative to that maximum. So, for this example, if the fractions of searches for the term “smartphone” out of all searches in SF over four weeks were 3%, 5%, 1%, and 4%, then their respective relative search indices would be 60, 100, 20, and 80 (since 3/5= 60% , 5/5= 100%, 1/5=20%, and so on).

Nevertheless, these relative measures are sufficient for comparing how shifts in search interest compare to rental price changes. Remember, our interest is in finding correlations, which do not have dimensions.  Against the Google Trends data, we pulled our own data on median rental rates (in dollars per square foot) for the relevant metropolitan areas over the same time periods.

We graphed the data for the Bay Area first:

Let’s get a bit formal: a little statistics

There appears to be a fairly strong similarity in how the search interest and the median rental rates move. But are we imagining things or is there a significant relationship here? To answer that question, we got nerdy so please excuse us if your eyes start to bleed! We calculated the correlation coefficient for the two data sets and performed a t-test to measure the significance of the correlation. From the t-distribution we found a two-tailed P value of less than .0001, which by conventional standards is extremely statistically significant. After repeating this for the New York, Philadelphia, and Atlanta data, we found that median rental rates in the other three cities also had statistically significant correlations with search interest.

Some takeaways

So what does this all mean? Well, the correlation between searches and rental prices implies that search interest acts as a proxy for rental demand.  In other words, keeping all things equal, if search volume increases, then prices increase (and vice versa).

Time Lags

Furthermore, the graphs clearly indicate that there is a time-lag between shifts in search interest and changes in rental rates.  Thinking about this practically, this makes perfect sense, as savvy prospective buyers typically research apartments before purchasing.

Seasonality

Seasonality is also interesting.  We can see that apartment searching research ramps up in late Winter before rental season begins and continues into Autumn, with consistently relatively low search interest in December. This, too, is consistent with our data which generally shows higher prices following periods of increased search interest.

Finally, here are a couple more pretty charts just for good measure:

NYC: we see a slight divergence in the search interest and price around late October 2012 that is partially due to time lags, but may also be due to the influence of Hurricane Sandy.