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Are Center City Apartment Rents Starting to Retreat?

The other day we received an email from our friend Lauren Gilchrist from the Center City District who pointed us to this article from the Philadelphia Business Journal documenting a big jump in the apartment vacancy rate, due to an abundance of new apartments coming on to the market.

She was curious if we had any data that showed if this increase in supply was manifesting itself in the form of lower rent prices. We knew that we had that data, but we had never actually generated a high-level report on rent trends in Greater Center City (we’ve mostly looked at neighborhoods, census tracts and the city as a whole to date.) So, we put one together to see what it looked like.

The trend graph above clearly shows that rent growth has slowed, and rents may in fact be starting to come down slightly. It’s hard to say where it will go from here but it’s definitely something we will be keeping an eye on as supply continues to enter the market.

We’d love to hear your feedback here, and as always you can check out our apartment deal ratings using our free service, RentMarket.

Heatmapping Philadelphia Rents by Bed and Bath

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Our last post which cataloged Philly’s most expensive places to rent in May got a lot of attention around the local real estate blogosphere (see here and here for examples). Our approach was to use rent price per square foot to rank neighborhoods’ relative rental costs.

While this approach is great for comparing neighborhoods to one another (look out for another post for June’s top 10 coming soon), it’s not as useful to individuals who are interested in understanding how much they can expect to pay for a particular unit in a particular neighborhood.

With that problem in mind, we created a new heatmap (shown above) where you can visualize the median rent price for a certain type of unit across neighborhoods (using bedroom and bathroom sliders to adjust the map values.) This map is updated daily to reflect the newest listings coming on to the market. Blank areas on the map reflect areas where we don’t have a enough data in the last 90 days to give a value.

We’d love to hear your feedback here, and as always you can check out our apartment deal ratings using our free service, RentMarket.

Philly’s Hottest Rental Neighborhoods in May

Lately, it seems as if neighborhoods have been all the rage here in Philly. Recent blog posts have been indicators of this trend.  There was this one in Philly Mag that identified the hottest neighborhoods in Philadelphia based on home sale velocity. Then there was this one in Curbed Philly that highlighted up-and-coming neighborhoods according to the sentiments of RedFin agents. What was most interesting about these posts, was that the top neighborhoods were ones that we would least expect. So, although some of these lists included the “usual suspect” neighborhoods, there were several sleepers. For example, Phoenixville, Brewerytown, and West Germantown were the top three up-and-comers according to agent sentiment at RedFin. In the Philly Mag piece, Graduate Hospital (of all places) was dubbed the hottest.  

As data geeks, these articles got us thinking. If Phoenixville is hot according to agent sentiment and Graduate Hospital is hot according to home sales, where is hot according to apartment rental pricing? Considering that we track this kind of stuff, we figured we would find out by ranking Philly neighborhoods according to apartment rental pricing. Some past experiences have indicated that heat maps are a great way to visualize pricing trends of different geographic areas. With that in mind, here are the results for the month of May (click image to get to interactive version of it):

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Who’s Number One?

At a whopping $2.41 per ft2, Fitler Square was the priciest rental neighborhood in Philly for the month of May. While the neighborhoods of the top 10 are mostly the “usual suspects” as referred to above (Rittenhouse Square, Old City, etc.), this was a mild surprise. As for what could be behind this, we have a couple of theories. The first is what we’ll call the “Naval Square effect”. For those not aware, Naval Square is an upscale megaproject by Toll Brothers. Despite being a condo project, owners there list their units for rent from time to time. Now although Naval Square is technically a part of the Graduate Hospital neighborhood (according to our neighborhood shapes), it does border Fitler. This could mean that some Naval Square price observations are getting included in Fitler, which would give it a nice price boost. The other theory is that since Fitler has a high ownership rate, the quality of units there may be higher than other parts of town. We’ll continue to monitor Fitler and tweak things like the minimum number of units per neighborhood. Stay tuned for updates in a future post.

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Our Up & Coming Neighborhoods

Although outside of the top 10, we thought it was worth noting that a couple of the Germantown neighborhoods are fairly highly ranked on the list. For example, Germantown – Westside came in at number 14 and West Central Germantown came in at number 18 for the month of May.  

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Though surprising to some non-Philly/Germantown folks, this may not even be a fresh trend based on other charts we have. Both of these neighborhoods have more or less held steady near $1.50 per ftfor the past several months. One does wonder whether these neighborhoods are uniformly hot or whether certain complexes are single-handedly propping up rates? In otherwords, are a few upscale complexes responsible for a higher-than-normal median rent? A closer look at complexes like Rittenhouse Hill, or Delmar Morris, or Cloverly Park shows that median rents there are all well north of $1.50 per ft2. We’ll take a deeper dive into these neighborhoods and report back accordingly.  

Leasing Season Is upon Us

As most real estate pros will acknowledge, May is actually the beginning of “leasing season”. From now until September, listing volumes and rental prices will be at their highest points for the calendar year. When considering academic calendars and weather patterns, this rhymes with common logic.  

There are a couple of neighborhoods affected by leasing season that we thought it noteworthy to mention. 

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The first is University City, which went from number 5 in the (unpublished) April rankings ($1.86 per ft2) to number 12 in May’s rankings ($1.62 per ft2). Students typically sign and make new leases around this time of year, so it follows logically that apartments marketed towards students might be higher in April than May. We will keep you posted in how this neighborhood continues to trend. 

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The second one is Society Hill, which went from number 19 in April to number 9 in May. Like University City, seasonality may be a culprit here.  In addition to a strong rise in median rental price, we saw more than double the number of listings in Society Hill from April to May. This is perhaps an indicator that a glut of leases in this neighborhood reset during this leasing season. We shall see. 

Methodology Notes 

Neighborhood Shapes – We were able to cluster our data in neighborhood shapes thanks to our friends at Azavea. We were certain to only include neighborhoods that had a minimum number of price observations, which might explain some holes in the map.

Unit of Measure – The unit of measure here is the median rental price per square foot per neighborhood. Although imperfect, we felt that the price per square foot was the best way to capture price levels across all unit mixes.  In other words, it was the best way to do an apples-to-apples comparison across all neighborhoods because one may have a lot of one bedrooms or studios, etc.

Bias – Lastly, note that we were careful to minimize any bias that outliers would cause by using medians and not averages of pricing data.

Parting Shots

In true startup fashion, we will continue to iterate our methodology to produce the best results for this. Stay tuned for future posts, as we will be reporting on neighborhood pricing movements on a monthly basis.  This should get interesting!

Interactive Bay Area Rental Price Heatmap by Census Tract

In our last post, our Chief Data Scientist plotted average rental prices per square foot in each ZIP Code in the San Francisco Bay Area through a quick hack using new mapping packages for R-Studio.  In true startup style, we have iterated on that and now have something better based on the feedback that we received.  

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How have we iterated you ask?  Well, we have iterated in three major ways:  

First, we have made the map web-based and interactive.  Now you can “mouse” over different areas to get information instead of tediously matching areas with our raw data.  Also, in response to some of the comments, it is now easier to see different cities and towns underneath our pricing information.

Second, in tune with our quest to provide the most granular information possible, we have mapped the data by census tract.  According to the Census Bureau, Census tracts are designed to be “relatively homogeneous units with respect to population characteristics, economic status, and living conditions… census tracts average about 4,000 inhabitants.”  We look forward to leveraging the public data available from the US Census to analyze things like income and demographics compared to rent in the near future.

Finally, whereas we presented the average price per square foot the last time, we presented the median price per square foot here.  We have discussed this in a previous post, but medians tend to be a more telling depiction of pricing than averages because there is a lower likelihood of an outlier skewing the sample.

As we noted in our last post, it is always somewhat astounding to see the disparities of rent pricing in seemingly close geographic locales.  But even more basically, it is incredibly eye-popping how high rents are generally.  Then again, this should come as no surprise, as according to David Crowe of the National Homebuilders’ Association, “[a]ll of the net addition to households since 2004 has been in rentals.”

To play with the web-based map, please go here.  Let us know what you think.

Bay Area Rental Price Heatmap by ZIP Code

We created Kwelia because residential real estate information is unstructured, messy, informal, and overall – not helpful.  A glaring example of this problem is the lack of granularity of real estate data on rentals in urban markets.  While it is not difficult to find indexes and other metrics on pricing movements in a city level, it is difficult (if not impossible) to find such information on pricing movements within a city.  Good luck finding pricing information per ZIP code, or better yet, by neighborhood.  

Well, consider this problem solved.  After tinkering around with a mountain of fresh rental data from the SF Bay Area, Kwelia’s Chief Data Scientist Chris Connell was able to plot the average rental price per square foot in each ZIP code on a map.  He describes the technical details for how he put it together in R on his blog.  Check out the map below or an enlarged version here:

It’s always interesting to view things geospatially.  One key takeaway (besides the obvious one that the SF Bay is PRICEY!) is that there is incredible price disparity among different parts of this massive MSA.  Although it is obvious that the East Bay is cheaper than the West, it is insightful to see that is it nearly $2/sqft cheaper.  For further granularity, check out below for the actual raw data that was used for the map (enlarged link and raw data).  

Can you find your ZIP?

Data Science Applications – Twitter Sentiment Analysis of 2012 VP Debate

While we here at Kwelia spend our days (and several nights) working to bring cutting-edge techniques in data science to residential real estate, every now and again, other interesting applications of our techniques arise.  It’s fall of a presidential election year, so much of America is preoccupied with the impending presidential election.  Few would disagree that the most entertaining components of the candidates’ campaigns are the debates.  It’s always a good time to watch them verbally joust against each other to solidify positions on issues and manifest their campaign rhetoric.

Who is the Winner?

But although the debates can be fun to watch generally, whether to poke fun at a candidate’s hair or to yell and call another a liar, they tend to get frustrating because there is often so much dissonance as to who addressed a topic better or even who won overall.  While the networks determine debate victory by polling citizens, there is typically crazy variance among the networks.  This variance phenomenon was amplified during last Thursday’s Vice Presidential Debate.  While no one disagrees that it was a close battle, who is to say (objectively) that one candidate completely pummeled the other candidate? 

Well, this was what different networks told us according to their polling.  According to the MediaMatter.org blog, “Snap polls released after the debate last night were mixed; a CBS poll of undecided voters found Biden winning 50-31, while CNN declared watchers “split” after their snap poll reported Ryan narrowly winning 48-44.”  If this wasn’t biased (or utterly confusing) enough, the different sides are pointing to different (unscientific) polls as indicators of their side’s victory.  For example, conservative media outlets are pointing to a CNBC.com poll that names Paul Ryan the winner – by a nose.  But when you unsheathe the methodology behind the poll, it is nothing more than a popularity contest akin to that of a high school student government election.  “Indeed, you can apparently vote multiple times across different browsers, and the results have fluctuated wildly over the past 15 hours. Last night, several conservative message boards and sites, including Free Republic and Tea Party Nation, posted links to the poll and encouraged their readers to vote in it. At various points, the poll has indicated that Ryan won the debate by twenty points and that Biden won the debate by 8. As of this writing, Ryan leads by 2 points with more than 190,000 votes cast.”

Let’s Find Another Way

So in order to decipher another way to objectively determine how the Vice Presidential fared through certain topics and even overall, our Chief Data Scientist decided to look beyond polling and analyze something more technologically forward…and even sexier.  The answer was Twitter.  His thought was that if you could measure the sentiment of all of the tweets transmitted during the debate, you could derive a fairly objective sense of what the sentiment is during certain topics.  Further, perhaps it may be possible to aggregate positive sentiments and crown a victor as well.

Sentiment Analysis in Brief

For those that aren’t up to speed on sentiment analysis, Wikipedia describes it as analysis that “…aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation (see appraisal theory), affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).”  Many of the techniques in sentiment analysis have been pioneered by renowned NLP professor Bing Liu.  In fact, Professor Liu has authored together software packages that automatically parse words that determine positive or negative sentiment for tweets.  These more or less set the standard for which words indicate sentiment.  While analyzing tweets may come off as a simple exercise, it is instead rather cumbersome.  Like our normal data routines, data must be collected, cleaned, and then ultimately presented in a format that facilitates further analysis.  Please check out Chris’s blog for some insights into his process behind this.

Methodology

The total data sample size for this experiment was 363,163 tweets, which was collected roughly every 60 seconds throughout the course of the debate.  As we must do during out typical data collection work, we had to remove several tweets in order to clean the data.  Duplicate tweets were removed, which left the final dataset tweetcount at 81,124 unique tweets whereby Biden had 52,303 tweets and Ryan got 28,821 tweets.  Each point represents the series of tweets that were gathered each minute and intuitively, the farther above zero a point is, the higher the positive sentiment of the tweets (and vice versa). 

Key Movements to Note

A quick analysis of the sentiment graph will demonstrate that there were some interesting peaks and troughs throughout the debate.  We’ve gone through some of the most drastic ones to correlate it with what was going on in the debate when sentiments either rose or fell to such levels:

21:08 – This was during the foreign policy portion of the debate.  You can note that Ryan’s sentiments were at lows during this early portion of the debate. 

21:31 – This was during the piece when Biden accused Ryan of requesting stimulus funds.  Ryan’s sentiments soared.

21:49 – This was during a Biden diatribe about what the Romney/Ryan camp may deem a small business (hedge funds perhaps?).  Ryan’s positive sentiments soared again.

22:26 – This was during the closing statements for each candidate.  Ryan’s negative sentiments reached lows.

Post-Debate – One interesting thing to make note of was the fact that although the debate only lasted an hour and a half, Chris was certain to continue the sentiment chart for an additional 30mins beyond the debate’s duration.  As you can note above, there were some interesting movements for each candidate’s sentiment.  It’s almost as if candidates’ respective sentiments were battling each other out for post-debate positioning. 

Going Forward

While this exercise proved more tedious than expected, we were quite pleased with the outcome.  If anything, it made watching the debates more entertaining.  Stay tuned for more, however.  Now that the code has been written, we will run the same analysis for the next two presidential debates – starting with tomorrow’s.  

Trends in Revenue Management – UNITS Magazine Excerpt

In advance of the impending Multifamily Revenue Management Conference, we were asked by UNITS Magazine to jot down some thoughts on some of the more salient trends in revenue management.  For those not familiar, UNITS Magazine is the largest trade magazine for the multifamily industry.  Please check out our section below.  For the full article complete with the opinions of others industry professionals, click here.  


Revenue Management:  From Exclusivity to the Mainstream

For nearly a year, we have been hard at work on what we truly believe is the next generation of revenue management software for the multifamily industry.  While developing our product, we have been able to witness widespread adoption seep through the industry as well as the emergence of some new trends.  Most notably, however, we have observed that revenue management software (and the culture that comes with it) is no longer the exclusive domain of REITs and large corporates.  As articles like “When Apartment Rents Climb, Landlords Can Say ‘The Computer Did It’” in the November 2011 New York Times will attest, Revenue management is slowly becoming the mainstream. 

Enterprise Software Leads Revenue Management Software

Interestingly enough, the trends that have allowed for this movement to the mainstream have largely been a reflection of innovations in enterprise software as a whole. Over the past several years, enterprise software has undergone a disruptive revolution to allow it to adapt to rapidly shifting behaviors in the workplace.As such, we have seen things like cloud computing and customer relationship management systems become products businesses cannot live without.We are confident that revenue management software will approach ubiquity among multifamily professionals as it continues to take its cues from rapidly evolving enterprise software.Three trends remain prominent in this road to ubiquity:

  • From the Ground to the Cloud – For the longest time, revenue management was synonymous with the clunky and expensive physical servers that housed data and allowed such systems to run.  Now, with the maturation of remote computing services, revenue management is migrating from the ground to the cloud.  The advantages are numerous as data can be manipulated easier, storage capacity is greater, integrations with other software systems are seamless, and cost structures are much lower.  Most importantly, though, access is no longer a barrier.  Revenue management systems can now be accessed from anywhere at any time to allow for 24/7 monitoring of portfolio data.
  • Revenue Management Software “Consumerized”? – In contrast to prior revenue management iterations with dizzying screens of numbers in columns and rows, the current crop of software is simpler and actually aesthetically pleasing.  These products are reminiscent of our favorite consumer-friendly websites, as they are now being built to favor design and user experience.  With simpler interfaces, smaller and leaner operations are less resistant to adopting them, given that they are not far removed from other software applications that are in regular use.
  • Advancing Methodologies – Finally, we are seeing substantial innovation in data science.  Predictive analytics, operations research, and other disciplines that comprise revenue management are advancing before our eyes.  As more attention has been given to these disciplines, novel techniques are emerging that are allowing for movement beyond the demand heuristic approach that more or less dominated the existing revenue management landscape.  In lockstep with the actual evolution of the techniques has been the ability to implement these techniques.  With the prevalence of open source software that allows users to actively contribute to the software development of statistical packages, data scientists are able to test new techniques quicker than ever before. 

Are Rental Prices Correlated with Gas Prices?: A Philly Analysis

Summer’s great, isn’t it?  The weather is warm, the barbeque is tasty, the moods are friendly, and most importantly, it’s vacation time.  Unfortunately, though, summers haven’t been the same since the US economy blew up in the mid 2000s.  Namely, the American vacation tradition has gone by the wayside for a lot of people.  These days, all we hear about are “staycations” or other creative alternatives to these annual events. 

At blame for the uprooting of this tradition has been the slow and painful rise of gas prices.  Anyone remember last summer when gas prices approached $5 in certain markets?  We definitely do.  According to the US Energy Information Administration, gas prices have been hovering around record highs since 2008.  As a matter of fact, outside of a dramatic dip in 2009, they have been at or around record levels since 2008.

What’s behind this recent meteoric rise in gas prices?  Well, it doesn’t take a trained economist to know that gas prices are largely an amalgam of macroeconomic conditions.  They are derivatives of oil prices, which are in turn largely the function of supply/demand dynamics and international policy.  Considering the turbulent events of the past few years, we suppose a rise in gas prices makes sense.  It’s pretty difficult to forget what happened in the Middle East/Northern Africa last summer with the Arab Spring and its aftermath…along with Gaddafi’s last stand in oil-rich Libya.  I guess if we combine those events with the tense nuclear environment in Iran, it should come as no surprise that there were indeed constraints in global oil supplies.

But despite the turbulence of the past several years, this summer has been markedly different in that there has been much less pain at the pump.  ”There’s some good news behind the discouraging headlines on the economy: Gas is getting cheaper,” cries out a recent MSNBC article.  As a result of the resolution of the aforementioned supply constraints and dire economic conditions in the EU to soften global oil demand, gas prices have lessened from extremes towards a new normal.  According to a recent Wall Street Journal article, “Many of the forces that drove gasoline up are reversing, and that is helping bring prices back down … .”  Gone with the “staycations”.  Bring on the vacations!

But deeper than vacations, the recent fluctuations in gas prices have begged an interesting question that we felt compelled to explore:  Is there any correlation between rental prices and gas prices?  We’ve recently blogged about record rents in many US markets.  As a matter of fact, yesterday we came across this article in CNBC discussing whether such markets are overheated.  Rents are soaring…gas prices have recently been soaring, yet are kind of backing for record levels…is there anything there?  

It follows logically that rental prices may indeed be correlated to gas prices when thought about qualitatively.  Although, this theory may only apply to apartment units near central business districts.  The thought here is that increased gas prices may lead to a reluctance toward living far from work/leisure.  According to Natalie Dolce of GlobeSt.com, “People are less likely to get in their car and drive … because the cost to fill up the tank has a dramatic impact on the total cost of their [rent].” 

In order to double-check our qualitative reasoning, we decided to use some data analysis techniques to confirm things quantitatively.  We chose the city of Philadelphia to test out such a correlation.  To do this, we compiled a data set of average gas prices in the Central Atlantic region over time and compared that against a data set containing average rent prices over time.  Our gas data came from the Energy Information Administration, while our rental data set of ~5000 Greater Philadelphia Area rental units came from our proprietary database.  Before running our analysis, it was important for us to figure out how to incorporate the lag between gas prices and rent prices.  Our guess n’ check process yielded that it took roughly a two weeks before gas prices had their full effect on rent prices.  For the sake of brevity, we are intentionally simplifying what is a technically complex process.  For some technical details as to how we did this, check out our data analysis blog here.  

Now let’s see how things turned out:     

Our data allowed us to measure correlation from April until mid-June.  With rent prices in gold and gas prices in black, the graph above confirms our qualitative suspicions: There was a strong positive correlation between rent and gas prices.  According to the graph, rent prices have been increasing at almost the same rate as gas prices from the beginning of this analysis until around mid-May.  At the end of the analysis, we are seeing gas prices begin a downward course, while rent prices are starting to plateau.  Interesting.

So, are gas prices a good leading indicator of rent prices?  I suppose the right answer is that it depends on the city analyzed and the time of the year.  We can guess that an LA or an Atlanta will have a stronger correlation than a New York City.  Nevertheless, the answer is that there is some positive correlation based on our analysis.   Now, will rents continue to follow gas prices?  We sense that they will, but we will be monitoring this going forward.  Look out for further posts on this.  

The Devil is in the Details: A Closer Look at Four Manhattan Neighborhoods

In our previous post, we attempted to establish several things.  Allow us to refresh your memories:  First, housing markets of late have been working in extremes.  Four years ago, we were discussing record lows in for-sale markets and these days, we are discussing record highs in certain rental markets.  Second, averages (or arithmetic means) can be misleading when used as indicators of pricing levels.  This is the result of potential biases that can come from outlier units like super-luxury condos or dirt-cheap rooms for rent in a dataset that can serve to bring averages either up or down.  The main takeaway was that medians are perhaps better indicators of price levels, as they represent the true middle number in a set.   

In the process of playing with the data to conduct last week’s mean versus median test, another key question was begged:  Is this intriguing gap between the mean and median number consistent throughout the entire borough of Manhattan, or will the sub-groups within Manhattan behave differently from each other?  As we know, Manhattan is a collection of several neighborhoods.  As a matter of fact, given the density in the borough, it could be argued that each neighborhood is almost like its own mini-city (think of the sheer number of people living in a neighborhood).  Given this, it makes sense that these mini-cities of sorts may have their own rental market dynamics that are distinct from other mini-cities within the borough…or even the borough as a whole.  

Before running the same test on certain neighborhoods as we did for the entire borough, we must first solve the eternal riddle of what a neighborhood actually is.  Our tenure in this business has taught us that neighborhood boundaries are nothing more than broker brainchildren perched neatly somewhere between art and science.  This is contrary to our seemingly naive beliefs from when we were laymen.  Because It is difficult to find the consensus as to where these boundaries are, we have found that ZIP codes are the surest indicator of them.  While ZIPs don’t perfectly rhyme with broker sentiment, they come close and that is all that matters.  

To conduct our intra-Manhattan neighborhood test, we plucked out four ZIPs that represent popular residential neighborhoods in Manhattan.  We chose 10024 (Upper West Side), 10003 (Union Square/East Village), 10036 (Midtown West), and 10021 (Upper East Side).  Let’s see how things turned out:

With the averages in “red” and the medians in “blue”, one can quickly note that in each of these neighborhoods, the averages are far north of the medians.  If you remember, this is similar to what occurred when we tested Manhattan as a whole, which should come as no surprise.  As the saying goes, however, the devil is in the details.  With a little bit more careful study, some interesting things can be taken away from these graphs.  Allow us to point a couple of things out:

First, in some neighborhoods, the averages and the medians did not move in lock-step.  An example of this behavior is evident in the 10024 ZIP (Upper West Side).  Here, the average price dropped dramatically from February to March while the median price remained flat.  A likely cause of this disparity could have been a drop in the number of luxury units on the market during this time period.  

Second, we can glean from these graphs that rents aren’t increasing in all corners of Manhattan.  Let’s look at 10038 (Midtown West) where prices have been falling since February.  The same phenomenon is occurring in 10021 (Upper East) to a lesser extent except for a small bump around the first of April.  These two ZIPs contrast the other two where means and medians follow each other likely as a result of a largely homogenous housing stock.   

Stepping back from these past two posts and thinking critically, one can conclude that much of the headline-grabbing commentary about rental markets lately is either misleading, exaggerated, or both.  While it is indisputable that rental markets are incredibly heated at the moment, there is some dispute as to how heated they truly are. Further, behavior within certain neighborhoods of Manhattan is divergent from the behavior of Manhattan as a whole.  For true residential rental insight, a superficial analysis of these markets will not suffice.  Stay tuned for further posts, as we will continue to bring novel and thought-provoking insights on rental markets to you.  

Means versus Medians: Are Rental Markets as Overheated as We Think They Are?

Following the US housing markets over the past several years has been akin to watching a giant pendulum swing one way and then another.  We are witnessing a dynamic whereby when one sector of housing cools off, another heats up.  No less than four years ago, news headlines were littered with descriptions of how the seemingly impermeable for-sale market crashed.  This one from a December 2007 article in Bloomberg was pretty endemic of what was going on, “US Housing Crash Deepens in 2008 After Record Drop”. 

These days, however, it’s different.  Now, we are seeing the opposite effect for rental markets.  Simply, they’re skyrocketing, especially in dense urban areas.  Check out this headline from an April 2012 WSJ article, “Rents Record in Manhattan” or this one from a few days ago in The Real Deal, “Reports: Manhattan rental market gets even tighter”.  This headline appeared in CurbedNY the other day, “Manhattan Rents Hit Record Highs as Busy Season Begins”. 

To us, though, it’s not the pendulum-like dynamic that’s the most interesting.  What is most interesting is how far the pendulum is swinging.  So, four years ago we saw record lows in certain for-sale markets and now we are seeing record highs in certain rental markets?  This dynamic got us here at Kwelia thinking:  Are rental markets truly as overheated or as pricey as the news outlets would have us believe?

Well, qualitatively, this makes some degree of sense.  For one, there exists a crazy disparity between available rental supply and the demand for rental product in big markets like Manhattan.  Perhaps as a result of tighter construction lending, there has been limited new apartment supply in certain urban markets.  According to CitiHabitat President Gary Malin, “[t]here are only about 2200 [rental] units are coming on the market this year…” in Manhattan.  This has been the lowest new supply figure in the past seven years for the borough.  On the other side of the spectrum, there exists unique demand factors that fuel a thirsty rental market.  Among these are urbanization trends and a tough mortgage environment to make it more difficult to purchase a home. 

But on the quantitative side, it becomes clear that looks can be deceiving.  We have noticed that consistent in all of these articles discussing record rent levels has been the use of average rents as the critical determinant of these levels.  Grade school math tells us that the average or arithmetic mean is simply the sum of a group of numbers divided by the total number of quantities.  While averages are commonly used in real estate circles to indicate pricing pressure, exclusive reliance on them can be misleading.  The reason for this is that extreme numbers (either high or low) can artificially inflate (or deflate) the average number in that set.  So if we can be more specific from our earlier question: Are Manhattan rents truly increasing beyond record levels or could there be an influx of uber-luxury units on the markets to skew the averages upward?

The quickest way to test for this is to derive median rents and see how they stack up next to the averages.  To take it back to grade school again, the median is the middle value in a set of numbers.  By examining the middle number in a set, we are less susceptible to bias by outlier numbers. 

Let’s test it.  Using our own data, we compiled a graph that compares moving averages and medians head-on for rents in the entire borough of Manhattan.

Despite some volatility week-to-week, our data corroborates that the average rent (in red) for Manhattan is mostly substantially north of $3000/month.  As the Wall Street Journal discusses, “The average Manhattan apartment commanded $3,418 in monthly rent in March, according to a first-quarter report to be released on Thursday by brokerage Citi Habitats, That is the highest rate since the firm began tracking such data in early 2002—topping the previous record of $3,394.”  

But now look at where our data indicates median rents (in blue) are.  Based on our numbers, median rents are around somewhere just south of $2600/month, which is a range that we highly doubt is near record levels.  Things get even more interesting by taking a step back and looking at the graph as a whole.  The gap between the the mean price and the median price is stunning, isn’t it?  The obvious conclusion is that average rents are getting notched up by outlier luxury units that command excessive valuations and can tend to be misleading when used as leading indicators.  

The lingering question resulting from all of this is where we are to go from here?  Are we to discard all of the aforementioned news articles and the analysis that came with them?  Well, clearly that’s not the outcome we were going for with the counter-analysis here.  Our goal is simply to demonstrate that blind adherence to averages can be deceiving and that although rents in towns like Manhattan are high, maybe they aren’t quite as high as we may think.  Generally, we can safely confirm our suspicions that averages can indeed mask what is really happening and that medians are a less-biased measure of price levels.    

We here at Kwelia are monitoring real-time changes in the rental markets that we cover.  Through our intelligence, we help landlords and property managers make smarter decisions.  Stay tuned to future posts as we will continue to share our insights on rental markets.