NAMED THE NATION’S BEST NEWSLETTER BY NAREE
MAY 2018 VOL 12 ISSUE 5
LOCAL MARKET SPOTLIGHT WILL AMAZON SAVE THE SWAMP? P11
MY TAKE ENCORE PERFORMANCE BY RICK SHARGA P22
SPLITTING THE ATTOM
DATA IN ACTION THE BEST MONTH TO SELL A HOME BY MARKET P27
WITH GREATER DATA ACCESS COMES GREATER DATA RESPONSIBILITIES P19
Contents
FEATURED ARTICLE
P1 HOUSING PRECOGS: BIG DATA PREDICTIONS BEYOND HEURISTIC HUNCHES
Real estate used to be a game of hunches. People bought and sold property because they had a sense of pricing, timing, and marketplace trends. Mortgages were made in large measure on the basis of past performance. Today hunches are out, big data is in, and the artificial intelligence revolution is taking the real estate world by storm with the promise of better leads and early access to future inventory. The nation’s capital is eagerly awaiting the selection of a candidate that could dramatically shift its fortunes in 2019 and beyond. Many in the region are hoping that one of three candidates located in the District of Columbia and its surrounding counties will beat out 17 others from around the country to be selected as the new location for Amazon’s second company headquarters known as HQ2. P11 LOCAL MARKET SPOTLIGHT: WILL AMAZON SAVE THE SWAMP? There has never been greater access to public property record data, but with that unprecedented access comes a greater responsibility in handling that data, particularly for companies that are collecting, curating and licensing the data, argues Nelda Green, vice president of data governance with ATTOM Data Solutions. P19 SPLITTING THE ATTOM: WITH GREATER DATA ACCESS COMES GREATER DATA RESPONSIBILITY America loves a good second act, and one of the least-noticed second acts in America today isn’t about an individual — or even about people, at least not directly. It’s about mortgage loans. And it’s a pretty amazing story, writes Rick Sharga, executive vice president at Carrington Mortgage Holdings, LLC. Sharga explains how many loans that fell into trouble during the housing downturn were able to be saved and are now back performing again. Home sellers have an enjoyed an extended sellers’ market over the last seven years, selling at a 2.9 percent premium above estimated market value on average, according to an ATTOM Data Solutions analysis of 14.7 million home sales from 2011 to 2017. But some days are better for sellers than others as this infographic illustrates. P26 BIG DATA SANDBOX: TOP 5 SMARTEST DAYS TO SELL YOUR HOME Home sellers have an enjoyed an extended sellers’ market over the last seven years, selling at a 2.9 percent premium above estimated market value on average, according to an ATTOM Data Solutions analysis of 14.7 million home sales from 2011 to 2017. But some days are better for sellers than others as this infographic illustrates. P27 DATA IN ACTION: THE BEST MONTH TO SELL A HOME BY MARKET P22 MY TAKE: ENCORE PERFORMANCE
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HOUSINGNEWS REPORT
SECTION TITLE
LEAD ARTICLE
Housing Precogs: Big Data Predictions Beyond Heuristic Hunches
BY PETER MILLER, STAFF WRITER
Real estate used to be a game of hunches. People bought and sold property because they had a sense of pricing, timing, and marketplace trends. Mortgages were made in large measure on the basis of past performance. Today heuristic hunches are out, big data is in, and the artificial intelligence revolution is taking the real estate world by storm with the promise of
better leads and early access to future inventory — translating into lower costs, less risk and bigger profits for the industry. The move away from housing market hunches to sophisticated predictive real estate analytics based on big data principles is led by a growing group of housing precogs that are relative newcomers to the industry with strong ties to Silicon Valley and
funded largely by venture capital. “We use predictive analytics and machine learning to analyze how likely a homeowner is to sell in the near future,” said Avi Gupta, President and CEO at SmartZip Analytics. “These techniques look at historical data — who has sold in the past — to identify, from several thousand data attributes, which ones may have been a factor in triggering those sales. And then, they
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2017 NEIGHBORHOOD HOUSING INDEX NEIGHBORHOOD HOUSING INDEX GRADE A B C D F
CLICK HERE TO VIEW INTERACTIVE VISUAL
look for owners that exhibit similar triggers to predict who is more likely to sell in the future.” Gupta added that “real estate is truly hyper-local, in that, the triggers that matter in a given neighborhood block can be different from the one next door, or even across the street. And these triggers can change from time to time even for the same neighborhood block. Hence, we have had to build hundreds of predictive models that look for various combinations of triggers to find the one that is the most accurate for each neighborhood across the country.” Personal Data Dossiers Back in 1971 — when many MLS brokers carried printed 3×5 cards to show inventory — the playwright Arthur Miller wrote that “too many information handlers seem to measure a man by the number of bits
“We use predictive analytics and machine learning to analyze how likely a homeowner is to sell in the near future. These techniques look at historical data — who has sold in the past — to identify, from several thousand data attributes, which ones may have been a factor in triggering those sales. And then, they look for owners that exhibit similar triggers to predict who is more likely to sell in the future. … Real estate is truly hyper-local, in that, the triggers that matter in a given neighborhood block can be different from the one next door, or even across the street.”
AVI GUPTA PRESIDENT & CEO, SMARTZIP ANALYTICS
of storage capacity his dossier will occupy.” Now such dossiers are far larger, vast electronic collections which detail our preferences in excruciating detail. Not just a tidbit here and there, but encyclopedic volumes of data ceaselessly gathered with clicks, links, cookies, tracking pixels, surveys, cell phone locators, loyalty programs, credit card purchases, and other collection
techniques. Companies, governments, and data brokers are accumulating unheard of volumes of data. Forget about gigabytes, petabytes, and exabytes. We’ve hit zettabytes — a measure equal to one trillion gigabytes.
“By 2025 the global datasphere will grow to 163 zettabytes,” says IDC.
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HOTTEST HOMEBUYER NAMES IN 2017 YOY PCT CHANGE IN HOMES PURCHASED IN 2017
1%
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tilting the playing field – you’ll need to understand how before your competitors do.” Proper Care & Feeding of AI Data is just part of the equation — and a relatively small part at that — when it comes to applying AI principles to predicting future real estate transactions, according to Brad McDaniel Co-Founder and CEO of Likely.AI, a company that provides AI-driven leads to the real estate and mortgage industries. “With the most advanced version of AI, called deep learning, which is what we use, only 10 percent of the final prediction decision is determined by the data itself,” he said. “That is because 90 percent of the predictive power comes from the extremely complicated interactions between the layers of neurons within the deep
“Generally speaking the growth of data across every part of the economy and our personal lives has provided us predictive modelers the ability to better understand how various pieces of a person’s life affect their decision-making. Everything is now on the table, from our social activity to current headline news to the types of products we buy online.”
ALEX VILLACORTA EVP AND CHIEF ECONOMIST, HOUSECANARY
“That’s ten times the 16.1ZB of data generated in 2016. All this data will unlock unique user experiences and a new world of business opportunities.” While data by itself has some innate value, it becomes exponentially more valuable when sorted and analyzed with artificial intelligence.
at HouseCanary. “the growth of data across every part of the economy and our personal lives has provided us predictive modelers the ability to better understand how various pieces of a person’s life affect their decision- making. Everything is now on the table, from our social activity to current headline news to the types of products we buy online.”
“Generally speaking,” explains Alex Villacorta, EVP and chief economist
“For a growing number of industries,” says McKinsey & Company, “AI is
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just based on Internet research,” said SmartZip’s Avi Gupta. “So, agents need to personally get in front of owners and build rapport over a period of time — which is hard to do for more than 300 to 400 owners. If an agent prospects with 400 owners that live next to each other, their chances of getting a listing in the next 12 months are the same as the organic turnover of that neighborhood, which averages 5 percent in the U.S. “Instead,” he continued, “predictive analytics affords us the opportunity to expand the aperture to about 2,000 homes, and then select the top 20 percent of homes with the highest likelihood to sell over the next year, i.e., the best 400 homes that are on average two to three times more likely to sell than average. This can double or triple the chances of winning listings.” Predicting Inventory While the bulk of all real estate inventory resides within local MLS systems, and while most properties
“With the most advanced version of AI, called deep learning, which is what we use, only 10 percent of the final prediction decision is determined by the data itself. That is because 90 percent of the predictive power comes from the extremely complicated interactions between the layers of neurons within the deep neural networks that we have created. We now live in a time where data availability is everywhere, but what you do with it is where the magic happens.”
BRAD MCDANIEL CO-FOUNDER & CEO, LIKELY.AI
neural networks that we have created. We now live in a time where data availability is everywhere, but what you do with it is where the magic happens.” Still the data is the foundation of the predictive analytics, and if the data is flawed the predictions based on that data will be flawed, notes HouseCanary’s Alex Villacorta. “When you have a lot of data and that data is ‘dirty,’ more data isn’t going to yield a better result,” he said. “You need to make sure that the data is
managed and refined in a way that serves your analysis.”
SmartZip’s Gupta pointed out that predictive analytics do not replace the important relationship-building aspect of the real estate business, but allow agents and brokers to better identify which relationships they should be building. “Real estate remains a relationship business, especially for home sellers, who typically choose agents based on trusted relationships and referrals, not
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are listed on an exclusive right-to- sell basis, there is non-MLS inventory available for sale. Sales outside the brokerage system can be significant. NAR esitmates that “8 percent of recent home sales were FSBO sales.” That’s not a big percentage but it does represent roughly 450,000 properties. Also, pocket listings are effectively outside the MLS system because they discourage, if not eliminate, cooperative sales. “Real estate is also highly competitive industry, compounded by low inventories across most major markets,” said Avi Gupta with SmartZip Analytics. “Homeowners are inundated with marketing from tens of agents in their area — so, agents have to find ways to cut through the clutter and stand out, be pre-emptive, and relate to the homeowner’s personal situation and needs/desires. Data and analytics are no longer a nice to have, they are essential to focus on the best prospects when it matters most.”
The lack of inventory is pushing up prices and reducing purchase opportunities in many areas. This may be one area where big data can be used to give homeowners a better understanding of the marketplace according to HouseCanary’s Alex Villacorta. “Many existing homeowners have chosen to remain in their homes in a ‘wait and see’ approach,” Villacorta said. “Big data can play a big role in helping consumers understand their local market and make informed decisions about whether now is indeed a safe time to sell a home, refinance, or make home improvements. In this age of economic and political uncertainty, big data and the insights gleaned from it can help to give context to the headlines and provide assurance to homeowners and buyers that they are making a decision that aligns with their risk appetites.”
One such example is a recent analysis of 14.7 million home sales from 2011 to 2017 that reveals the best month and day to sell a home based on the average premium above estimated market value sellers get when selling on each month and day. While the results of the analysis at the national level support cWonventional wisdom of selling in the spring or summer — best month to sell is May and the best day to sell is June 28 — the best times to sell vary by local market influenced by weather, retiree migration and other factors. (See more details in this issue on Page 27.) Likely.AI’s Brad McDaniel explains that “I don’t think big data will impact inventory shortages in the short term, but combined with AI prediction forecasting tools, it will be a huge help to developers moving forward when conducting feasibility studies to making final decisions about what product mix they should build, and where they “Many existing homeowners have chosen to remain in their homes in a ‘wait and see’ approach. Big data can play a big role in helping consumers understand their local market and make informed decisions about whether now is indeed a safe time to sell a home, refinance, or make home improvements.”
AVERAGE U.S. HOMEOWNERSHIP TENURE (YEARS)
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ALEX VILLACORTA EVP AND CHIEF ECONOMIST, HOUSECANARY
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Q1 2008
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will build. This will lead to optimal real estate options tailored to the specific future needs of each area. “Also, the government on a county level could benefit from AI predictive tools. One example would be predicting future property tax revenues, based off which properties will sell. This will allow them to have more accurate tax revenue forecasts and budgets.” AI Meets Mortgage Lenders Mortgage lending — like real estate brokerage — is an enormous business. In 2017, according to the Urban Institute, first lien originations totaled
$1.8 trillion. That’s a huge number but it’s 14 percent less than the $2.1 trillion originated in 2016. It also represents a major shift in lender profitability. The Mortgage Bankers Association (MBA) reported that in 2017 “independent mortgage banks and mortgage subsidiaries of chartered banks made an average profit of $711 on each loan they originated in 2017, down from $1,346 per loan in 2016.” The reason for lower profits in 2017 is the combination of reduced volume and fixed costs. Production per company fell from 11,106 loans in 2016 to 8,882 originations in 2017 according
to the MBA. In the same period costs per loan rose from $7,209 to $8,082.
Millions of homes have been financed and refinanced at 4 percent and below during the past few years so why would owners refinance in 2018 when rates are higher? Refinancing represented 49 percent of the market in 2016, a figure which is expected to reach 27 percent this year and even less in 2019 according to the MBA. Loan originations on residential properties decreased 19 percent in Q4 2017 led by a 34 percent drop in refinance originations, according to ATTOM Data Solutions. While declining volume is a concern for lenders, an equally fundamental issue involves the product they sell. While all real estate is different — in theory it’s indestructible, unmovable, and always unique plus it reflects such psychological and social values as status and ego — that’s not the
“I don’t think big data will impact inventory shortages in the short term, but combined with AI prediction forecasting tools, it will be a huge help to developers moving forward when conducting feasibility studies to making final decisions about what product mix they should build, and where they will build.”
BRAD MCDANIEL CO-FOUNDER & CEO, LIKELY.AI
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case with mortgages. Nobody has a photo of their mortgage. A VA loan is a VA loan. Lenders can originate more of them tomorrow. What counts most for borrowers is low cost and convenience, the ease of application. Increasingly, what counts most for lenders are digital platforms, the use of artificial intelligence, and an ability to overcome clutter, to be the first and only lender with whom borrowers interact. Lower costs for borrowers Borrowers want both convenience and lower rates. The coming competition will not be for the fastest loan application but the one which produces the lowest cost, the app that allows borrowers to pick from competing mortgage offers. “For a typical $250,000 loan,” says Freddie Mac, “the average expected savings from only one additional quote is $1,435.” It adds that “80 percent of borrowers who obtain one additional rate offer will save between $966 and $2,086. The average expected benefit increases to $2,914 if the borrower receives five rate quotes. Eighty percent of borrowers who obtain five offers will save between $2,089 and $3,904.” You can see the disruption opportunity. Well-funded fintech firms will spend $1 million a week promoting platforms where borrowers save and lenders compete. Lower costs for lenders Given origination costs of better than $8,000 per loan lenders have every
reason to use AI not only to generate more business but also to reduce production expenses. A major target will no doubt be personnel expenses, which according to the MBA averaged $5,346 per loan in 2017. Speech recognition software will play an important part in the cost-cutting process. Once clunky and mechanical, such systems have improved greatly in recent years. In India it’s estimated that the human-like Google Assistant program has received 450,000 marriage proposals. For loan officers virtual speech programs and other automated services represent both promise and peril. The promise is that a single loan officer can be more productive because the application process is increasingly automated. The peril is that there are only so many loan
applications to be written. If some loan officers — or fintech systems — are more productive it means large numbers of the nation’s 306,000 loan officers are not. Instead, they now represent an expense of almost $19.5 billion a year (306,000 loan officers x an average salary of $63,650 according to the Bureau of Labor Statistics). The idea that there will be so many loan officers in five or 10 years is improbable. Ditto for large numbers of mortgage underwriters. “I don’t think targeting marketing using predictive AI will lower a marketer’s expense,” said Brad McDaniel with Likely.ai. “It will rather have a huge impact on their return on investment. Think about the difference between bombing that occurred in World War II and that happens today. Do you think today
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BIG DATA DEFINITIONS: DISTINCTIONS WITH A DIFFERENCE
“Artificial intelligence, big data and machine learning are helping us reduce risk and fraud, upgrade service, improve underwriting and enhance marketing across the firm. And this is just the beginning.”
Artificial Intelligence : A term coined in 1955 by computer scientist John McCarthy, “artificial intelligence” can be described as the ability of computer systems to complete tasks historically done by people. Think of speech recognition as well as practical applications such as stock trading, the ability to read x-ray images, or fit eyeglasses. Data : An old business theorem says “that which can be measured can be managed.” The result of such efforts include measuring, counting, mapping, and numbering. Data may have value by itself (think of the score from one football game) but data becomes powerful when individual items can be processed together to reveal trends, directions, totals, averages, etc. Deep Learning : “Deep learning,” explains Mathworks, “is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands- free speakers.” Big Data : One football game by itself has meaning, it’s data. The games played over an entire season represent more data. The results from thousands of games played since 1960 are big data – but only after they have been arranged, examined, compared, poked, and messaged. Machine Learning : Machine learning can best be compared with the Borg, aliens from the Star Trek franchise who assimilate other cultures. As they explain, “we are the Borg. Your biological and technological distinctiveness will be added to our own.” In a similar sense, machine learning takes what it has learned today and adds that data to what it has accumulated before to create a new data set and what might be called bigger data.
JAMIE DIMON JP MORGAN CHASE
CHAIRMAN AND CEO IN HIS 2018 SHAREHOLDER LETTER
bombing done from planes in conflict is less costly? No, it is probably more costly, but 1,000 times more effective. I believe the same applies to targeted marketing, rather than spending a little on a lot of people, spend more on the right people. This concept applies to both direct marketing and digital marketing, even though it is somewhat counter intuitive for digital marketers who usually focus on reach rather than depth. As the saying goes ‘you can’t bet too much on the winning horse!’” Democratizing Disruption Data-based marketing is now like pollen, it’s everywhere and to make such systems work we have vast and growing armies of technologists. reduce risk and fraud, upgrade service, improve underwriting and enhance marketing across the firm,” JP Morgan Chase Chairman and CEO Jamie Dimon explained in his 2018 shareholder letter. “And this is just the beginning.” “Artificial intelligence, big data and machine learning are helping us
Predictive Modeling : The use of existing data to forecast future trends.
In banking, as one example, McKinsey & Company reports that “AI’s ability to detect anomalies among millions of transactions helps bank risk officers eliminate false positives that are a drain on productivity.”
JP Morgan has almost 50,000 technologists so where’s the opportunity for small firms?
The answer is that not only is our technological base growing it’s also being democratized. Fintech — financial technology — is everywhere
At first it might seem as though big data is reserved for big players.
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and you need not be a financial colossus to succeed. Far from Silicon Valley the Millennium Bank of Ooltewah, Tennessee, saw profits grow by 120 percent between 2014 and 2017 through the use of data- based decision-making. “The bank can now answer small loan requests in seconds,” reports American Banker. “This speed in decision-making means Millennium’s lenders are spending less time to complete smaller, uncomplicated deals while freeing them up to work on the more complex loan requests.” “Small firms may find it challenging to build their own data and analytics teams in-house, but there are now a preponderance of analytics- and data- as a service companies that offer turnkey solutions for just about every application,” explains Alex Villacorta with HouseCanary. “In many instances, these services can be paired with pre-curated data sets that allow small firms to marry their own data with large data sets that would have been prohibitively expensive to acquire from their original sources.” Nimble fintech lenders can utilize AI efficiencies while at the same time avoiding many of the costs faced by traditional lenders — mammoth personnel armies, huge branch systems, ATMs, legacy expenses from past years, etc. Moreover, there’s no reason why massive and well-known online players cannot enter the lending industry. Anyone for a Google mortgage or an Amazon HELOC? How did this happen?
BEWARE OF THE TECHLASH
It might seem as though fintech players with their computers and math are the wave of the future but not so fast. To make AI work you need data and right now there’s a growing sense that maybe companies know too much. As a result the fintech revolution may be slowed but not stymied by a growing techlash, hurdles which won’t be so easy to overcome. Data value “Technology innovation in the real estate industry is robust,” said NAR General Counsel Katie Johnson in April. “The notion that real estate isn’t highly competitive and listing data not readily available is unsubstantiated,” she argued. “To the contrary, a wealth of listing data is available to consumers and technology companies from a multitude of sources, and Realtors provide their clients and consumers with more real estate information today than has ever been available.” “If information sharing allows consumers to avoid payments to real estate agents for listings they contributed to the MLS or for brokerage services provided, then broker incentives to cooperate and share information are diminished,” says Fredrick Flyer, who has served as an economic expert for Fortune 500 companies as well as the U.S. Federal Trade Commission, Department of Justice, and the Department of Energy. “Conversely,” he adds, “setting limits on access to brokers’ data by third-party aggregators can enhance broker competition and in turn make consumers better off.” Translation: listing data has value — and brokers have long felt they should capture more of it. If brokers gain more revenue from their data it also means data users will face higher costs. Privacy There is a growing debate regarding what’s private and what isn’t. There is no right to privacy listed in our Founding Documents, instead the concept was outlined in an 1890 article from the Harvard Review by Samuel Warren and Louis Brandeis arguing that “the common law secures to each individual the right of determining, ordinarily, to what extent his thoughts, sentiments, and emotions shall be communicated to others.” Later, in 1928, in a Supreme Court decision, Brandeis famously defined privacy in the Olmstead case as “the right to be let alone – the most comprehensive of rights and the right most valued by civilized men.” But maybe listing data is “too” widely available, so available that it devalues broker services and worth.
Given Victorian notions of privacy many were outraged when Scott McNealy, then CEO of Sun Microsystems, said in 1999 that “you have zero privacy anyway. Get over it.”
Not everyone agrees.
“Users should be in control of how their data is used,” wrote Bill Gates in 2002. “Policies for information use should be clear to the user.
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BEWARE OF THE TECHLASH
Users should be in control of when and if they receive information to make best use of their time. It should be easy for users to specify appropriate use of their information including controlling the use of email they send.” “We’ve never believed that these detailed profiles of people — that has incredibly deep personal information that is patched together from several sources — should exist,” said Apple CEO Tim Cook, speaking on MSNBC. He added that “privacy to us is a human right.” Today privacy is making a comeback – in Europe. The General Data Protection Regulation (GDPR) is scheduled to go into effect across the EU in late May and it may impact the privacy debate here. “Users will have greater control, including the ability to learn what information companies have on them,” reports The Hill. “The GDPR will also codify what’s known as ‘the right to be forgotten,’ meaning consumers will be able to order web services to delete their data or stop distributing it to third parties. The rules will also require companies to give users the ability to easily revoke consent for handing over personal information.” The GDPR shows that a practical privacy standard is possible, a concept which may gain traction in the U.S. if the public tires of intrusive data collection efforts. Regulation In the same way that credit reports are no longer accorded the status of state secrets, data controls may soon become far more accessible to consumers. The Facebook Russia debacle, the Equifax credit breach involving almost 148 million accounts, the loss of as many as 1 billion Yahoo user database files, and the collection of data from children all suggest that regulation — what is allowed and what isn’t — is an emerging issue. Even Facebook founder Mark Zuckerberg has stated in congressional testimony that it’s “inevitable that there will need to be some regulation.” “No federal law spells out what companies trading in personal information can do with user data,” says Axios. “No federal agency has clear jurisdiction over writing rules for Internet companies. And public concern about personal data falling into the wrong hands has only recently swelled.” “U.S. adults,” says the Pew Research Center, “are roughly twice as likely to express worry (72 percent) as enthusiasm (33 percent) about a future in which robots and computers are capable of doing many jobs that are currently done by humans.” If data regulation is inevitable, if it can get past First Amendment arguments, then it’s likely that new rules will make data collection more transparent. Some collection efforts will be restricted or prohibited. Consumers could have a bill of rights which allows them to see and correct data (think of credit reports). Maybe a “do not data” list will evolve. One thing is certain: with more regulation the cost of data will increase. At the same time we may see a shift from today’s standard. Now you can opt out but only if you can find who’s collecting data in the first place. In the future there may be a requirement that consumers must opt in before data can be collected. Errors As much as artificial intelligence and predictive modeling get things right such systems can produce unwanted results. As an example, a CNN investigation found that “ads from over 300 companies and organizations — including tech giants, major retailers, newspapers and government agencies — ran on YouTube channels promoting white nationalists, Nazis, pedophilia, conspiracy theories and North Korean propaganda.” Security The Internet and computers have more security holes than electronic Swiss cheese. Viruses, malware, worms, Trojan horses and hackers are lurking everywhere, sometimes in the employment of foreign governments. “More than 317 million new pieces of malware — computer viruses or other malicious software — were created last year,” reported CNN in 2015. “That means nearly one million new threats were released each day.” But, right now, that’s not the case.
Juniper Research predicts that 5 billion personal data records will be stolen in 2020, up from 2.8 billion last year.
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Will Amazon Save the Swamp? SPOTLIGHT: DISTRICT OF COLUMBIA BY JOEL CONE, STAFF WRITER
would diversify it and it would attract other businesses,” said economist Stephen Fuller, director of the Stephen S. Fuller Institute for Research on the Washington Region’s Economic Future, Schar School of Policy and Government at George Mason University. The three potential candidates in the metro area are Montgomery County, Maryland; Washington D.C. itself; and Northern Virginia. Over the past few years Jeff Bezos, founder and CEO of Amazon, has laid the groundwork for selecting the
company’s new headquarters, with Amazon’s final top 20 candidates announced in January. Now considered the world’s richest billionaire according to the Forbes 2018 list, Bezos has already established a presence in the D.C. metro area. First he bought the Washington Post back in 2013 for $250 million. He followed that with paying a reported $23 million to purchase the former Textile Museum in 2016, for the purposes of converting into a
The nation’s capital is eagerly awaiting the selection of a candidate that could dramatically shift its fortunes in 2019 and beyond. Many in the region are hoping that one of three candidates located in the District of Columbia and its surrounding counties will beat out 17 others from around the country to be selected as the new location for Amazon’s second company headquarters known as HQ2. “We have the kind of talent in the categories they are looking for. It would be good for this economy. It
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WILL AMAZON SAVE THE SWAMP?
residence. Located in the Kalorama neighborhood, and made up of two homes on the National Register of Historic Places totaling a combined 27,000 square feet, when renovated it will be the largest home in the nation’s capital. Best for Housing Not all Amazon employees have a multi-million dollar budget for buying a home, so Housing News Report looked at median home prices and six other factors affecting housing and quality of life in each of the three D.C.-area markets to determine which might be most appealing for prospective homebuyers and homeowners. Among the three HQ2 candidates in the region, Northern Virginia — comprised of 15 counties and cities in the D.C. metropolitan statistical area — had the lowest median home price at $390,000 followed by Montgomery County, Maryland at $400,000 and the
District itself at $520,000, according to ATTOM Data Solutions.
County ranked No. 2 on the list behind Raleigh, North Carolina, making it highest among the three in the D.C. metro area. Northern Virginia ranked No. 9 on the list and the District ranked No. 17. If any of the three local venues are chosen to house the new HQ2, it could be a game changer to that area’s economy and housing market, bringing as many as 50,000 jobs and increased demand for more housing.
But homes were more affordable in Montgomery County thanks to higher wages there, according to an ATTOM Data Solutions analysis of price-to- income ratios. Based on all seven factors considered in the housing and quality-of-life analysis — home prices, appreciation, affordability, school scores, crime rates, property taxes and environmental hazards — Montgomery
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“We have the kind of talent in the categories (Amazon is) looking for. It would be good for this economy. It would diversify it and it would attract other businesses.”
STEPHEN FULLER DIRECTOR, STEPHEN S. FULLER INSTITUTE FOR RESEARCH, GEORGE MASON UNIVERSITY.
AMAZON HQ2 FINAL 20 CITIES REAL ESTATE MARKET RANKINGS
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State
Q4 2017 Median Home Price
5-Year Home Price Appreciation
Price to Income Ratio (Affordability)
Avg School Score
Crime Rate to Natl Avg
Raleigh
North Carolina
$235,000 $400,000 $220,000 $346,500 $150,000 $361,000 $275,000 $138,000 $395,000 $284,619 $130,000 $285,000 $776,000 $140,000 $223,000 $204,000 $520,000 $1,445,000 $670,000
26%
3.69 3.59 3.68 5.86 2.55 4.93 5.16 2.89 3.83 5.21 2.60
1.13 1.17 0.99 0.92 1.06 1.06 0.87 0.85 1.04 0.93 0.81 0.95 0.85 0.65 0.92 0.52 1.17 1.09 0.60
95.99 33.93
Montgomery County Maryland
8%
Atlanta Denver
Georgia Colorado
69% 73% 36% 67% 52% 14% 16% 71% 88% 27% 48% 69% 21% 41% 50% 114% 246%
303.28 179.28 214.60
Pittsburgh
Pennsylvania
Austin
Texas
98.00
Nashville Columbus
Tennessee
125.98 189.53
Ohio
Northern Virginia
Virginia
52.66
Dallas
Texas
193.66 286.73 232.69 142.55 256.21 231.14
Indianapolis
Indiana Florida
Miami
5.77
Los Angeles Philadelphia
California
14.51
Pennsylvania
3.09 4.43 6.47 6.47
Chicago Newark
Illinois
New Jersey
42.42
Washington
District of Columbia
315.97
New York
New York
14.79
82.33
Boston
Massachusetts
9.57
115.21
CLICK HERE TO SEE THE ENTIRE TABLE
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WILL AMAZON SAVE THE SWAMP?
large numbers of people are moving to the metro area, they tend to move out when they see that the economy is doing better elsewhere. And those outmigration numbers have recently outpaced the in-migration numbers, resulting in a net loss of population, according to Fuller. In the March issue of the his “Washington Economy Watch” Fuller noted that the region’s economy is growing in a positive direction. Still there is concern since the federal government had 6,600 fewer jobs between Trump’s inauguration in January 2017 and January 2018. “These jobs area important because they leverage home buying, buying a nicer car and clothes,” Fuller explained. “The average household income here is $55,000.” Although unemployment in the District has steadily declined in the first three months of 2018 to 5.6 percent in March, job growth in January 2018
“If (Amazon choosing any of the three D.C.-area markets) happens my job security just got a lot better. We work in every submarket in D.C. and at every price point. It’s a good end result for whatever municipality gets HQ2. The question is how quickly will they see the benefits for the broader market.”
CLINT MANN PRESIDENT, URBAN PACE A REAL ESTATE SERVICES FIRM FOR BUILDERS AND DEVELOPERS
Swamp Drain Diluting Jobs Although still strong, the broader economy in the D.C. region has been showing some signs of weakness, according to Fuller. “All the job growth has been in the non-federal sector,” he said. “The job base is diluting in terms of salary. We’re adding more lower-paying jobs and losing higher-paying jobs.” Fuller noted that the D.C. area has the highest share of Ph.Ds. as a percentage of the workforce in the country, but added that outmigration of those Ph.Ds. and others is a problem. While fairly
For Clint Mann, president at Urban Pace, a company that provides sales, marketing, leasing and advisory services for builders and developers, the choice of any of the three D.C. venues would be good news. “If that happens my job security just got a lot better,” said Mann. “We work in every submarket in D.C. and at every price point. It’s a good end result for whatever municipality gets HQ2. The question is how quickly will they see the benefits for the broader market.”
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D.C. METRO HOME SALES PRICE TRENDS
MEDIAN SALES PRICE
$400,000
$450,000
$380,000
$400,000
$350,000
$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
$0
was primarily due to the dominance of the area’s three private sectors: professional and business services, education and health services, and leisure and hospitality services. It’s a job mix that favors lower value- added jobs, Fuller said. Housing Stretched Thin Like nearly everywhere else in the country, housing inventory is stretched thin while home prices continue to rise in the Washington, D.C. market. For March, the Greater Capital Area Association of Realtors reported a 1.6-month supply of inventory and a 9 percent decrease in the total number of homes for sale from a year ago. The median days on market was 13 days or less, four days quicker than a year ago. Median home prices in the metro area hit a new post-recession peak of $380,000 in Q2 2017 — although still 5 percent below the pre- recession peak of $400,00 in Q3 2005 according to ATTOM Data
“If the property is priced right and in a desirable area, it is not uncommon to have multiple offers. It’s overwhelming for a lot of buyers.”
KENT FOWLER SALES ASSOCIATE, LOGAN CIRCLE OFFICE OF COMPASS
Washington, D.C., covering the District, Northern Virginia and Montgomery County, Maryland. “If the property is priced right and in a desirable area, it is not uncommon to have multiple offers,” he said. “It’s overwhelming for a lot of buyers. I think D.C. has become a more desirable place to live in the past 12 to 15 years, but it’s also more expensive to live. We’re seeing people moving here who wouldn’t have considered it 15 years ago.”
Solutions, and median home prices increased 2.9 percent from a year ago in Q1 2018 — the eighth consecutive quarter with a year- over-year increase. The Northern Virginia Association of Realtors reported that days on market are down more than 19 percent to 42 days and the inventory level is down to a 1.64-month supply, an almost 21 percent decline from the year before. Low inventory translates into bidding wars on the best properties, despite the escalating prices, according to Kent Fowler, sales associate with the Logan Circle office of Compass in
Off-Market Condo Conversions Fowler said he is seeing a lot of
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WILL AMAZON SAVE THE SWAMP?
investors coming into the D.C. area, trying to buy off-market properties.
directly involved in the gentrification of the D.C. metro area. Focused on multi- unit commercial properties including mobile home communities, assisted living and self-storage, she spends a lot of time revitalizing properties.
able to get as many as two or three condos out of it.
“My experience is that most of them are looking for single family or what we call fee simple. They’re buying at the right price that works for them,” he said. “What they’re trying to do is find it and have more control over it to renovate it.” He sees investors looking to turn row homes into condo conversions, and depending on the size of the row home, Fowler said an investor may be
Fowler said one of the main concerns for end buyers in any transaction is whether the appraisal will come in at the sales price. Most of the time it does. Also, he is seeing a fair amount of all-cash transactions, although the majority of properties are still sold with financing. Banking on Baltimore A longtime investor, property manager and consultant, Tammy Phelps is
“We’re taking distressed assets and re- positioning them,” Phelps said.
As executive director and founder of the Capital Cities Real Estate Investment Association, Phelps said her group’s members are playing an active role in improving neighborhoods, particularly in Baltimore, a very strong market for revitalization because of Johns Hopkins University and the University of Maryland. Phelps also noted that Baltimore City ranked first in rental yields during the first quarter of 2018 at 28.6 percent, according to the ATTOM Data Solutions Q1 2018 Single Family Rental Report. “There’s a lot of flipping going on there. Baltimore is still affordable for first-time homebuyers, but is only 30 minutes from D.C.,” she said. “It still has some rough blighted areas but investors are revitalizing them.” There are other neighborhoods in the metro area such as Deanwood (the northeastern corner of the District) that were once considered to be ghettos but are now seeing a lot of rebuilding, she said. ATTOM reported that the 21239 zip code in Baltimore City was one of the 50 top zip codes for home flipping rate in 2017, with flips accounting for 19.4 percent of all home sales during the
“There’s a lot of flipping going on there. Baltimore is still affordable for first-time homebuyers, but is only 30 minutes from D.C. It still has some rough blighted areas but investors are revitalizing them.”
TAMMY PHELPS FOUNDER, CAPITAL CITIES REAL ESTATE INVESTMENT ASSOCIATION
2017 HOME FLIPS BY ZIP HEAT MAP HOME FLIPPING RATE (PCT OF TOTAL SALES)
-2.5%
31.5%
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year, although down 11.4 percent from 2016. With a median purchase price of $70,218 in 2017 and a flipped price of $165,000, investors earned a 135 percent gross return on investment per flip. The average days to flip there were 218 days in 2017. “Baltimore was one of only a dozen counties in the country in which a buyer would need less than 15 percent of their income in order to purchase a home using conventional financing,” Phelps noted in a recent presentation. Two zip codes in the District also made the top 50 markets for home flipping rate in 2017. The 20032 zip was highest in the metro area with flips accounting for 26.5 percent of the homes sold during the year, while in the 20019 zip, flips were 25.7 percent of the homes sold. Investors in the 20032 zip realized a 112.4 percent ROI in 2017 while those in the 20019 zip realized a 108.7 percent ROI. Prince George’s County, Maryland had three zips in the top 50 for home flipping rate in 2017. Those were the 20710 zip where flips accounted for 21.0 percent of all home sales, the 20748 zip where flips accounted for 20.3 percent of all home sales , and the 20746 zip where flips accounted for 20.1 percent of all home sales . Construction’s Slow Comeback Cranes dot the skyline of the nation’s capital these days, but economist Fuller said construction is not back to where it once was. “Our building permits are not anywhere near where they were before the recession,” he noted. “It
may have even slowed down a bit in the past year. We’re a little overbuilt in rentals. Vacancy rates have gone to 5 percent from 3.5 percent. It’s different by submarket.” Still, area homebuilders remain confident in the market’s potential, according to Mann. “Builder confidence is strong in that we are still a very undersupplied market. The bigger challenge in D.C. is construction costs. And land costs have
gone up, making it more difficult to get projects off the ground,” he said.
Plus, although there is demand for new housing, it is for a smaller portion of the market. Due to lack of land, building in the District is constrained, affecting affordability. Uber-Fueled Construction There is development happening further out, and thanks to Uber, Lyft and other ride sharing platforms, Mann
“It’s a challenge and an opportunity for us if we’re willing to build in the fringe neighborhoods and offer everything buyers are looking for. Then we’re able to attract buyers from all across the city. Affordability is still driving many of these decisions.” CLINT MANN PRESIDENT, URBAN PACE, A REAL ESTATE SERVICES FIRM FOR BUILDERS AND DEVELOPERS
WHERE HOME PRICES ARE AFFORDABLE (OR NOT) FOR AVERAGE WAGE EARNERS MEDIAN HOME PRICE AFFORDABLE FOR AVERAGE WAGE EARNERS? NO YES
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WASHINGTON-AREA CASH SALES SHARE
D.C. METRO AREA
DISTRICT OF COLUMBIA
NORTHERN VIRGINIA
MONTGOMERY COUNTY, MARYLAND
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017 YTD 2018
said consumers are now willing to go to other areas further from the capital.
“Washington has a housing market where the demand for housing is harder to measure because so much of the population is transient. As for the homebuying market, the more expensive homes in the $800,000 plus category, a significant portion is purchased with foreign money. We have a lot more than most markets because we have 180 foreign consulates here.
“It’s a challenge and an opportunity for us if we’re willing to build in the fringe neighborhoods and offer everything buyers are looking for,” he said. “Then we’re able to attract buyers from all across the city. Affordability is still driving many of these decisions.” Additionally, there is new residential construction going up around the metro stations, particularly the new silver line that will service Dulles International Airport. High rise residential development is planned for Tysons Corner in Fairfax County, Virginia, and there is plenty of residential already built and being built around the Reston town center in Reston, Virginia.
STEPHEN FULLER DIRECTOR, STEPHEN S. FULLER INSTITUTE FOR RESEARCH, GEORGE MASON UNIVERSITY.
In order to draw millennials and young professionals away from the District,
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