2014’s Most and Least Expensive Metros

As our growing database continues to reinforce, real estate is inherently local. Reflecting this notion, many of our previous posts revolve around analyses within cities. Whether comparing rental price trends to the Case-Shiller 20-city index or mapping out rental affordability, we’ve primarily focused on market-level data for our posts. Recently, however, our curiosity has led us to dig into broader questions on a national level. How do different cities, or even regions, compare on a variety of features? We have barely begun to showcase the geographical breadth of our data–until now.

The Kwelia rental database includes hundreds of metropolitan statistical areas (MSAs) nationwide, enabling us to provide insights into nearly every rental market in the United States. When taken together, that range allows us a uniquely macroscopic view of the national rental market. Although there are many dimensions (such as amenity composition, rental descriptions, unit types, etc.) on which we can compare different metros, we will save those for future discussions.  For this post, we’ll focus on one high-level characteristic: price. Here, we consider prices as the median USD per square foot for a metropolitan area. Without further ado, the most expensive metro areas in the United States for 2014:

Most Expensive Metro Areas in the USA 2014

Most Expensive Metro Areas in the USA 2014

Riding the wave of the oil boom in the Bakken shale formation, Williston, ND catapulted to the top of the list of most expensive metropolitan areas in 2014. At $2.74/sqftmedian rent, it is a full $.16/sqft more expensive than the New York metropolitan area ($2.58/sqft) and the San Jose-Sunnyvale-Santa Clara area (also $2.58/sqft). The surging demand for oil workers to tap the Bakken shale and a scant supply of people (mostly men) willing to endure the hard work, grueling hours, and remote location sent median wages Williston, ND soaring north of $75,000. High disposable income coupled with a limited supply of housing subsequently pushed median rents to the impressive level observed. Nevertheless, whether the market there can sustain those rents in the face of new apartment construction and tanking oil prices remains to be seen in 2015.

Least Expensive Metro Areas in the USA 2014

Least Expensive Metro Areas in the USA 2014

Among the rest of the most expensive metropolitan areas are few surprises, with the New York area and Silicon Valley rounding out the top four. Honolulu, HI cracked the top five with a median rent of $2.27/sqft. Notably, with the exception of Williston, ND, all of the top 15 most expensive metro areas are coastal (or in the case of Hawaii, an island).

Unlike the most expensive group, the least expensive MSAs are predominantly Southern/Midwestern and landlocked. Albany, GA (home of Ray Charles, Deion Branch, and Paula Deen, among others) had by far the cheapest median rent ($.50/sqft) of any metro area in the United States. However, with inflation-adjusted median income at just under $40,000 and unemployment above the national average, it also wasn’t the most well-off.

Taking the above example, “most expensive” isn’t necessarily synonymous with “least affordable”, and vice-versa. One’s purchasing power varies with real income, so an analysis of which MSAs are the most affordable requires consideration of real income (and typically also the price of a fixed basket of goods, which would include more than just rent).

Which metro areas are the most and least affordable? Check back soon for the next blog post in our ongoing exploration of the national rental market.


Does “Apartment Chopping” Make Sense?

I just read a thought-provoking article over at The Brooklyn Reader reporting that across Brooklyn property managers are chopping up existing apartments to add additional bedrooms. The basic logic of this makes sense–the more renters an apartment can support, the more potential income to be spent on increasing rents. However, the apartment will also become more cramped and less valuable on a “per renter” basis.

Naturally, I wanted to take a more data-driven look at this. I decided to use Bed Stuy as a case study, since it was one of the neighborhoods mentioned in The Brooklyn Reader’s article. Here are some median rent statistics for Bed Stuy in the last 60 days taken from our Competitive Intelligence product (click to enlarge):


The spread in rent between one and two bedroom apartments in Bed Stuy is currently at $305 dollars. So, it seems at first glance that if you can inexpensively add a bedroom to an existing one bedroom, you should definitely do so. But, number of bedrooms is not the entire story in terms of apartment value. One bedroom apartments are more attractive to renters, at least in terms of what the market signals in terms of rent per square foot: $2.12 for one bedrooms vs. $2.00 for two bedrooms. So, you can expect to lose around 5.6% in per square foot value after converting a one bedroom to a two bedroom apartment in Bed Stuy, since the total number of square feet is not going to change.

Furthermore, the converted two bedroom is likely to have to closer to 800 sqft (the one bedroom median) than 1,000 sqft (the two bedroom median) since, after all, it used to be a one bedroom. Let’s split the difference and assume it’s a bigger one bedroom at 900 sqft.: that’s still 10% less than the median two bedroom, so we’re not going to end up with a full-sized two bedroom apartment.

Taking those adjustments into consideration, we can expect the actual increase in value to be much lower: $1,924 – (5.6% of $1,924) – (10% of the previous result) = ~$1,645. That’s only $26 more than the one bedroom average, and almost certainly not worth the cost of the conversion.

This is obviously a simplistic analysis, but I was skeptical that you could take an existing apartment that’s presumably been designed for a certain number of occupants, shoe-horn in an extra bedroom and get something substantially more valuable. The basic reason for this is that it does not take into account the fact that you’re not really adding value from the renter’s perspective, and what the renter is willing to pay for an apartment is all that really matters. You’d be better off adding nicer finishes, which do add a lot of incremental value (but that’s another post.)

This analysis is also specific to Bed Stuy, and it may actually make sense in other areas where the numbers look different–say where two bedroom apartments have a higher or similar per square foot value than one bedroom apartments, or where the difference in size between typical one and two bedrooms isn’t so pronounced.

If you want to dig deeper into this kind of data & analysis, check out the free trial of our Competitive Intelligence product!



Why the Zillow-Trulia Merger Is Meaningless for Real Estate Innovation

The NY Times (and others) are reporting today that Zillow is going to acquire Trulia for $3.5 billion in stock. This is a massive merger that will no doubt effect massive change in the real estate media landscape. This news is not surprising given Zillow CEO Spencer Rascoff’s goal of “[creating] a portfolio of real estate properties, becoming more along the lines of an IAC/InterActiveCorp.”

The comparison to a media holding company such as IAC is a good one. Zillow’s primary innovation has been to bring real estate marketing online. Now, their path to future growth is to get more eyeballs on their content via acquisition.

And by all accounts, the new Zillow has a healthy market opportunity in front of it:

“The revenue of the merged company — $341.2 million last year – represents only a small part of what he [Rascoff] reckons is the $12 billion the real estate industry spends on marketing each year.”

However, this isn’t going to do much to affect the way actual real estate business is conducted.

Zillow is a real estate marketing company, but the real estate industry is not primarily a marketing business. Zillow may be content to own this niche, but there are much bigger opportunities out there to innovate in real estate.

Property management is a $69 billion annual business in the US alone. A big chunk of what property managers do could be conducted online, but currently isn’t.

Real estate development and investment is a order of magnitude larger than that, and is vastly under-served by the incumbent software and data offerings.

And yet, relatively few startups have attempted to serve these markets.

At Kwelia, we’re excited to capitalize on Zillow’s missed opportunity. We’re building the data platform that powers true innovation in real estate, and we’re starting with rental housing data. If you’re ready to join the next wave of real estate innovators building on our platform, drop us a line.


The Best Times to Rent: Apartment Dynamic Pricing Patterns

“You should always buy plane tickets on Tuesdays,” my friend tells me.

Or was it strictly business hours in the first half of the week? Conventional wisdom abounds on the ideal times to purchase a flight, as consumers have long known that airline prices tend to change from day today and sometimes even more quickly. Airlines are hardly alone in employing these tactics though; many e-commerce sites also use dynamic pricing to respond to fluctuations in demand by adjusting prices algorithmically.

Should you always buy plane tickets on Tuesdays? As a result of these dynamic pricing methods, some services have leveraged their data to help consumers pick good times to buy (Kayak and Bing, for example, try to forecast flight price movements in part using query volume.)

Is there a best time to rent an apartment? At the end of last month, an article from Time Magazine examined how apartment prices, too, can change rapidly. Take a look at the infographic below, and read on to find out when you’re more likely to find an apartment deal:


First, how prevalent is dynamic pricing in the apartment industry? Estimates suggest that roughly 20% of apartments nationwide use some form of revenue management software to adjust prices. Upon examining the relationship between apartment pricing and time from a sample of more than 1.5 million listings selected from our national database, we found some patterns.

Much like airline tickets, there are ideal days to find an apartment — while Sunday and Tuesday were the most expensive days, Monday and Friday were the least expensive. Furthermore, the time of the month makes a difference. Prices rose throughout each month by about .4% total, with the majority of that increase occurring between the first and second weeks of each month. Similarly, there are more and less expensive months in which to rent. We found that prices are lowest in January, rise consistently until August, and hold steady through the end of the year. At their summer peak, prices were nearly 4% higher than in January.

So when should you rent? If you can afford to be flexible on timing, look for listings around the beginning of the new year. In general, stick to the first week of the month, and preferably look on a Monday or on a Friday. Of course, you can also always sign up for our free apartment ratings tool to find great deals using our statistical models. Happy hunting!


The “Housing Affordability Crisis”: Affordable Low-Income Rentals Without Federal Assistance?

Recently there has been some discussion of rental affordability and the increasing share of income that American households pay for rental units. Back in January, we examined rent as a share of income in adding that data as well as income data to our maps. Since then, some have discussed the particular hardships faced by low income renters, such as in a wonderful interactive map and report by The Urban Institute on the evolving housing availability landscape for extremely low-income (ELI) households. The bleak housing outlook for low-income households has led some to theorize on the culprit of the “housing affordability crisis.” Some argue that the supply of apartment units has simply not kept pace with the trend of Americans increasingly choosing to rent, causing low income renters to spend increasingly large portions of their income on housing. Meanwhile, Next City, in an interesting analysis of the Philadelphia market, concludes that poor housing affordability in Philadelphia is a symptom of low income rather than of insufficient rental supply.

As we do not gather public housing or Section 8 information, we can’t and won’t speculate on the causes or the magnitude of a dearth of low-income housing. That said, it is clear that households are paying a more sizeable fraction of income toward housing than in previous years, and that finding a rental for less than 30% of income is increasingly challenging. Given that we have a large database of rental listings, we decided to investigate the following: all things being equal, how difficult would it be for an extremely low-income household to find an affordable rental without federal assistance?

To answer this question, we first have to set out the assumptions we are making:

  • Households are four-person households as in The Urban Institute study.
  • The per-county ELI cutoff levels for household income are the same 2012 values used by The Urban Institute.
  • Rent above 30% of annual income is considered burdensome, as commonly defined.
  • Given a household of four, it was assumed that any unit needed at least 2 bedrooms.
  • Lastly, assuming “all things being equal”: it is possible that market-clearing prices would change if federal assistance did not exist.

Above: The ELI cutoffs for a four-person household in various counties around the U.S.

Given these assumptions, we set out to find just how many listings in a county could satisfy our criteria. For each county under consideration, we looked at listings from the past three months where the listings satisfied the price and bedroom parameters set out above. We then compared the number of these listings to the number of ELI renter households in the respective county to find the ratio of ELI listings to ELI renter households.

Above: The maximum monthly rent a four-person ELI household could pay to not exceed 30% of income by county.

As it turns out, these rentals are indeed very rare. In all 16 counties we examined, none of the counties had more than 1000 such units. Furthermore, the proportion of the number of these cheap listings to the number of ELI renter households did not exceed 1% in any of the 16 counties under consideration

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Incidentally, the proportion of listings to ELI renter households appears to buttress Next City’s determination that Philadelphia’s problem rests with income more than with rental supply, as Philadelphia has more than twice the proportion of ELI rentals compared to comparable cities (again, this is strictly private housing and does not include Section 8). According to The Urban Institute, of the top 100 counties in the U.S., Suffolk County (Boston) had the smallest gap between the number of affordable listings and the number of ELI renter households. Why then is the gap larger in our data? The Urban Institute figure includes federal housing assistance, which ours does not. Thus, once again, these figures on affordable listings should be understood with the knowledge that the presence of federal housing assistance can vastly affect the number of affordable units. Nonetheless, it is interesting to see how scarce many of these units on the low end of the market would be relative to the number of households potentially looking to rent them if there were no federal assistance.


Markers of Gentrification: Mapping Rent as a Share of Income

Our Kwelia heat maps have been a great way to visualize rental prices with a detailed, city-wide view of median prices per square foot, and today the maps are getting additional features. Now, in addition to the median price per square foot overlay, you can explore every rental market by median income as well as by median rent as a percentage of median income. Simply toggle the “switch layers” button on the bottom left portion of the map and select your desired data layer to look around.

Where does the data come from, and what are these metrics?

Edit: An earlier version of our maps did not contain proper attribution for OpenStreetMap contributors. We apologize for this oversight and have updated our maps accordingly.

Both layers use median household income data from the 2012 5-year American Community Survey for the median income. The median rent / median income layer provides a quick glance into the affordability of an area. While not the same as the share of income paid by renters, our median rent-to-median income ratio indicates what percentage of their income the average person in that neighborhood would have to pay to rent the average apartment in that neighborhood. In other words, while the share of income paid by renters will stay bounded between 0-100%, it is quite possible (and does happen) that the median rent-to-median income ratio exceeds 100%. This could potentially happen for a variety of factors, including: multiple people splitting rent for an expensive apartment, time lags between the older income data and the up-to-date Kwelia rental data, or simply too few rental price observations in a neighborhood to provide an accurate median rental rate.

Median rent over median income is most useful for relative comparisons, so to provide some explanation for what “30% of income” means, we have also provided median income data in the info hover panels as well as a separate median income map layer to contextualize the rent share of income data.

Finding hot neighborhoods with rent as a share of income:

While we initially mapped the ratio of rent to income to see how this metric varied within and across cities, we found that it unexpectedly gave us a view into another category: neighborhoods with rapidly increasing rents. Because of the time lag between the 2012 5-year ACS income data and the real-time Kwelia rental data, the ratio of rent-to-income in neighborhoods with rapidly increasing rents is outsized relative to other neighborhoods. We’ve collected some examples of hot neighborhoods from cities across the United States below. Does the median rental share of income in your city highlight up-and-coming neighborhoods too?

San Francisco


New York




Las Vegas






Los Angeles





Kwelia Rent Price Trends vs. The Case-Shiller Home Price Index


As Hamlet probably said while perusing Danish real estate: “to rent, or not to rent, that is the question.” Prospective renters (or homeowners) can attest that the decision to buy or to rent involves a complex consideration of a number of factors, including the time horizon for the living arrangement, expected changes in rental and home prices, and mortgage rates (fortunately, both the New York Times and Trulia conveniently provide tools to assist with some of the math). While the market dynamics and personal factors that inform the decision to buy or to rent are complex, we’d like to briefly explore one of the factors mentioned above: the evolution in rental and home prices, and then investigate the relationship between the two.


In order to compare home prices and rental prices, we first need yardsticks for both. For home prices, we used the 20-city indices from the Case-Shiller 20-city composite index, and for rental prices we used our Kwelia data (of course), as well as some nationwide Consumer Price Index (CPI) data for rent of primary residence.

The Case-Shiller Index–Useful for Tracking Home Values

As mentioned last time in our piece about correlations between search interest and rental prices, the Case-Shiller index provides a benchmark measurement for home values in the U.S. There are multiple indices, including the national index (using quarterly single family home values), and the 20 and 10-city composite indices (as well as an index for each of the 20 cities), using monthly home values. The indices use a “repeat-sales” methodology by including homes that have sold at least twice in order to quantify an appreciation in value. Lastly, the Case-Shiller methodology involves measuring changes in price while keeping quality constant, i.e., ignoring physical changes to homes in order to measure the real appreciation in price (for example, a home increasing in price by 20% because an addition was added is not the same as a home gaining 20% in value with no improvements).

Not quite apples-to-apples…apples-to-pears?

For the rental data, we used a monthly average of the weekly median rental prices in the same 20 cities as the Case-Shiller data. However, it should be noted that while the Case-Shiller home price index provides a “real” (i.e., constant quality) measure of housing prices from month to month, the rental data is not quality adjusted. That said, the duration of the comparison for each city is generally one to two years, over which period the impact of quality changes in rental stock should be relatively minor on a city-wide basis.

Are Home Prices and Rental Rates Related?

Note: while others have written in detail about prices and rents, this exploration should be treated as more of a “lab-exercise” to illustrate an effect empirically rather than as a formal study.

The hypothesis (“Here be Economics”)

Purchasing a house or an apartment instead of renting can be a more economical choice depending on the time horizon under consideration. For a very short timespan, it is often cheaper to rent, but for longer timeframes there is a breakeven point (at which you would be indifferent between buying and renting) after which it is cheaper to have purchased the house or apartment rather than renting. For example, it might take a decade for a house to become the cheaper option compared to renting for the same period of time. How long it takes to reach this breakeven point depends on the conditions of the market, including tax rates, interest rates, expected capital returns, the risk premium in the housing market, etc. For instance, low interest rates make ownership more attractive, while a risky housing market and high interest rates incentivize renting instead of purchasing.

Are rental rates and prices related in theory? In a classical frictionless market, the total cost of ownership for a house is comparable to an equivalent rental rate. This is called the owner’s equivalent rent, or the amount which an owner would pay in a competitive rental market to rent his own home. Any home has an associated hypothetical rental value, and any rental has an associated hypothetical purchase value. As such, in a competitive market in the long run, the discounted returns to renting an apartment should be equivalent to the total purchase value.

Therefore, we would hypothesize that rental values and home values are positively correlated (i.e., the two would move in the same direction in general). If rental rates and prices were to deviate too much, then arbitrage opportunities would act to bring the two closer together. For example, if rental rates were very high and housing were very cheap, then one could take advantage of this price difference by buying a house and renting it out. This would have the effect of: 1) Increasing the supply of rentals, bringing down rental rates and 2) Decreasing the supply of houses, increasing the price of purchasing a home. The opposite would also hold true.

However, because there are frictions in the market and buying and selling is expensive, one would not expect complete arbitrage (i.e., price differences would persist). Furthermore, although rental rates and prices are related in theory, there are other independent factors in both the rental and housing markets that determine prices for each. Because of this, we would expect only a moderate positive correlation on average in the long run. Does the data bear this prediction out?

The Results


Correlation between monthly Kwelia rent values and Case-Shiller index (r)















San Diego








San Francisco




Los Angeles
















Las Vegas




































New York




From a quick look, we can observe that 15 out of 20 of the cities showed a positive correlation between rental rates and home values. However, by looking at the t-values to determine which of these correlations is significant, we can eliminate the bottom 5 cities (whose correlations are not statistically significant), leaving us with 13 out of 15 correlations both positive and statistically significant. Excellent. Not only are the majority of the correlations positive and significant, but most of them have r-squared values above .6, indicating that a significant amount of the variation in the data is accounted for in the model.

But what about Charlotte and Minneapolis, which both have statistically significant and negative correlations? We thought about this for a while (and welcome any ideas!), and after investigating a few hypotheses, have settled on one theory for now: it’s an accident. The timeframe for the data used in both of those cities is just under a year, which is necessarily a short-run analysis. If rental rates and home values were to drift apart over the course of a year, the market wouldn’t have time to take advantage of the price difference and bring them closer together.

Ok, that sounds fine in theory, but data talks–does this long-term positive correlation between rents and home prices exist? To find out, we compared the 10 and 20-city Case-Shiller composite indices against the Consumer Price Index data for rent of primary residence (which is a real-valued index), from January 1st 2000 until now. Unfortunately, the CPI is not segregated by metropolitan area, but rather an index of national rental values. Nevertheless, as most rentals are in cities, we compared the CPI to the 20 and 10-city composite indices rather than the national Case-Shiller index to get the most fair comparison.

Although one can clearly see the explosion (and subsequent bust) in home values surrounding the mortgage crisis, there is as predicted a moderate (r~.3), statistically significant (t~4), positive correlation between rental values and home values over a long duration. This helps add evidence to our theory that the observed negative correlations were essentially an artifact of the short-run analysis, and that given sufficient long term data, a positive correlation would prevail as prices adjust.

The trend in Denver was positive as well.

Concluding Thoughts

While the evidence from this little experiment suggests that rents and prices are in fact positively correlated, it’s important to remember not to overstate the significance of the result. First, as noted above, the lack of a city by city “real-valued” rental index makes one-to-one comparisons with the Case-Shiller index difficult. Furthermore, the limited timeframe under consideration also prevents one from drawing too strong of a conclusion. Lastly, it is worth mentioning once more that the determinants of prices in both markets are diverse, and that the correlations found here serve more as an illustration of a specific connection between rentals and homes, rather than indicating a strong causal effect in determining prices.

That said, it is still interesting to “verify” these theoretical relationships with some actual data. Those interested in seeing the graphs for the remaining 18 cities can see them below, and those curious should check out some relevant papers at the end of this post. Finally, the scope and depth of our data is constantly growing, so we hope to provide even more robust analyses in the future!



San Francisco 

San Diego



Los Angeles

Las Vegas









New York


Appendix for the curious

Outside Data Sources:

Case-Shiller data:


Rent CPI data:


Relevant Literature:

“The Long-Run Relationship Between House Prices and Rents,” Joshua Gallin.


“Run-up in the House Price-Rent Ratio: How Much Can Be Explained by Fundamentals?,” Kamila Sommer, et al.



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 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.


Philly’s Best Neighborhoods for Deals: An Analysis of Amenities and Price

If you’ve been following our coverage of the Philadelphia rental market, you already know which of Philadelphia’s rental neighborhoods are the hottest in May and in the summer. However, there’s more to consider when looking for an apartment than just the price and the location; amenities are important too. To shine some light on this aspect of the rental market, we first asked: which amenities are available, and what fraction of apartments feature them? After selecting a number of luxury amenities to investigate, we looked to the data as usual for some insights into the distribution of rental amenities throughout Philadelphia.

Is “luxury” actually rare?

Overall, the single most frequently listed amenity throughout the rentals was “stainless”, as that amenity was present in nearly 11% of the listing observations (“stainless” likely refers to stainless steel kitchens).  Listings for rentals with doormen were the least common, comprising roughly 1% of the listing observations. Nevertheless, between one-fifth to one-quarter of all rental listings had at least one luxury amenity.

Next, we looked at how the term “luxury” is used compared with more specific feature descriptors–while the presence of granite, stainless, or a doorman are all objective and verifiable, the scope of what is considered “luxurious” could prove a bit more subjective. Did the use of the term “luxury” line up with how often we would expect to see it, given what we’ve observed about the frequency of luxury features? It would appear that, yes, there is some truth in advertising–only about 4% of apartment listings indicated that the unit had “luxury” features, suggesting the term is used judiciously.

Is there a better time to look for a luxury apartment? From the chart above, we can see that the proportion of listings with any luxury amenities stayed between one-fifth and one-quarter for the past ten months. Although the winter months saw a slight decline in the frequency of luxury apartments, the impact of seasonality appears to be relatively low, at least in this year.

Let’s talk cost/benefit

Ritzier amenities tend to come with ritzier price tags, however. Lest we forget, apartment rentals, like anything in life, demand tradeoffs. With that in mind, which neighborhoods have listings with the most “bang for the buck?” Given the information on neighborhood amenities newly in hand, as well as our previous coverage of Philadelphia rental neighborhood prices, we only had to combine the two to produce a ranking of the “best deal” neighborhoods in Philadelphia:

Best Deal: Mill Creek

A quick note on methodology: the “best deal” ranking above is derived by taking the price per square foot in the neighborhood and dividing by the median frequency of having any luxury amenity (lower ratios are better). This metric provides insight into the most affordable places to find apartments that also have luxury features. Lastly, to keep the rankings relevant to current market conditions, the prices and amenity frequencies used are the three-month moving average of their respective monthly median values.

The top-five “best deal” neighborhoods overlaid on our rental price heat map

The map and the rankings show that there are a number of neighborhoods with affordable but well-equipped rentals, especially West and North of Center City. In fact, the majority of apartments listed in Mill Creek and Haverford North (numbers one and two, respectively, on our list) have at least one luxury amenity. While Mill Creek has consistently posted high figures for the proportion of its rental stock with a featured amenity over the past ten months, the fraction of such listings in Haverford North has increased dramatically since the fall of 2012, especially since this April:

There’s no such thing as a free lunch

The impressive increase in the median frequency of luxury listings in Haverford North over the past five months has not come without a catch. As this screenshot from our Competitive Intelligence product shows, the rise in the proportion of luxury listings has been mirrored by a dramatic increase in median rental rates. From April to June (and persisting into August), the median rent per square foot in Haverford North roughly tripled.


There’s definitely more than one angle to look at all of this data, and plenty more data to analyze, so check back for more on rental markets around the country.


Philadelphia’s Hottest Rental Neighborhoods: The Summer Update

Back in June, we wrote about the beginning of the summer “leasing season” with our report on “Philly’s Hottest Rental Neighborhoods in May”. With Labor Day rapidly approaching, let’s take a look at the rental market action so far.

While prices cooled off a bit from June to early July, the prognosis for the rental market in 2013 continues to be positive, with growth in apartment rental demand outstripping available stock, as noted in this Philly.com article. Furthermore, vacancy rates appear to be retreating after a jump earlier this summer. To meet the growth in rental demand, 2013 expects to see the addition of more rental units than last year, such as the newly opened 2116 Chestnut property that we will touch on again below. Overall, the rental market heated up through July–but which areas saw the greatest demand? Without further ado, here are the hottest Philadelphia rental neighborhoods for July:

Who’s at the top?

Rittenhouse tops the list of the most expensive rental neighborhoods in Philadelphia this time around, with a median rental price of $2.44 per square foot. Nine of the top ten priciest neighborhoods from May made it to the July list, with the sole newcomer this time being University City, which nabbed a close second place to Rittenhouse with an impressive rental rate of $2.41 per square foot. Rittenhouse saw prices rise 17.9% as vacancies decreased through the month, in part due to fewer available units in the new 2116 Chestnut building by The John Buck Company that opened earlier this summer. Previous number one Fitler Square remained in the top four, with Callowhill staying put in third place. Perennial contender Old City rounded out the top five, with prices in the top ten up overall month-to-month.

University City and Rittenhouse Rates Grew Significantly from June to July

Movers and Shakers

While rental rates rose overall across the board, the rental market in University City experienced the greatest price growth of any Philadelphia neighborhood from June to July, with median rates increasing an amazing 78.5%. Neighboring Woodland Terrace prices also grew impressively, at a 37.4% rate from June to July. Why the large change in both of these neighborhoods? As discussed previously, the seasonality of the academic year likely plays a significant role in prices in the University City area, with nearby Woodland Terrace absorbing some of the demand. The demand from the influx of students and faculty preparing for a new academic year has driven up rates as the summer leasing cycle concludes.

The impact on Woodland Terrace (unpublished June rank: 61) is particularly striking, as it vaulted 41 spots up the list to number 20. Rents in Woodland Terrace increased 37.4% from June to July on the strength of seasonal demand and dwindling vacancies.

Summer Trends in the “Top 10”

Having highlighted the recent price growth in several rental neighborhoods through July, let’s take a look at how the market has shaped up for the top ten hottest rental neighborhoods in Philly so far this summer.

Prices adjust to decreased listings, but lag supply changes

We can see that prices climbed into May, fueled by increased demand as “leasing season” began. The stock of listings increased markedly in May, likely driven by the addition of new units as well as seasonal leasing cycles bringing more apartments onto the market. This surge in rental listings is reflected in swooning prices through June into July, despite reduced availability of units. However, prices rallied from July, adjusting to the decreased number of units on the market. Nevertheless, there is a perceptible time-lag between changes in listing supply and price adjustment (a good reason to adopt Competitive Intelligence!).

Methodology Note: The constituents of the “ten hottest” rental neighborhoods in Philadelphia change somewhat from month to month. To track how the rental market for the top ten most expensive neighborhoods evolves from month to month, the prices given in the chart above are the weighted average of the median rental rates of the members of the top ten priciest neighborhoods for each month.  This provides an approximation for a “characteristic” price for the ten most expensive neighborhoods in Philadelphia in a given month.

Closing Remarks

Check back for future updates as we continue to follow the hottest rental neighborhoods; we’re constantly keeping an ear to the ground to track neighborhood pricing movements.