Chief economist for Zillow says “homeownership is not for everyone”

The chief economist for Zillow suggests we need alternatives to homeownership for low-income American residents:

All this leaves us with a conundrum: Overall, homeownership is a tremendous boost to millions. But in some specific cases, it simply does not deliver as advertised. Depending on circumstances, homeownership is not for everyone. And our steadfast belief that homeownership is always the better option has led us to worry less about the one-third of Americans that rent,leading to a crisis in affordable rental housing.

Please don’t get me wrong. None of this is to say that lower-income Americans should not aspire to homeownership, nor be given opportunities to access its tremendous benefits. But we also need to be steely-eyed about the realities and foster a wider diversity of options on housing, crafting innovative solutions that address the reality we face, not the one we imagine.

If we truly believed this, we could do different things. We could focus on the creation and maintenance of more affordable rental housing. We could find innovative new ways to build wealth, aside from homeownership. Given the prevalence of single-family rentals in the aftermath of the recession, we could explore the feasibility of renting-to-own on a wider scale. We could narrow and sharpen our focus on addressing the fundamental sources of inequality that drive differences in homeownership in the first place.

Yet, even with the strong negative effects of the recent economic crisis/housing bubble, I wonder if it is easier to promote homeownership than it is to advance other policies. Here are several reasons why this might be the case:

1. Americans really do seem to prize homeownership. Homeownership is closely tied to the American Dream, making this issue both politically and culturally important. As far as I know, every president since the 1920s has promoted homeownership. Suggesting that everyone can’t access the American Dream can be problematic.

2. Renting may be a good short-term solution but because of the status conferred to homeowners, renters receive the opposite sentiments: transient, less committed to their community, more prone to social problems, etc. Plus, how many wealthier residents want to live near cheaper rental housing?

3. Speaking of cheaper housing, affordable housing is a very contentious issue. Where will these units be built? Wealthier neighborhoods and communities want little to do with affordable housing. Which developers will go this route rather than chasing bigger profits with larger and more expensive housing units?

4. Getting at the fundamental issues behind the the differences in homeownership is a huge task. Which shall we tackle first – Race? Social class? Residential segregation? Large disparities in wealth? Unequal access to resources?

Perhaps the price to be paid in housing bubbles is more palatable to those in charge than the other options…

Deciphering the words in home listings; “quaint” = 1,299 square feet, “cute” = 1,128 square feet

An analysis of Zillow data looks at the text accompanying real estate listings:

Longer is almost always better, though above a certain length, you didn’t get any added value — you don’t need to write a novel. Over 250 words, it doesn’t seem to matter. Our takeaway was that if you’ve got it, flaunt it. Descriptive words are very helpful. “Stainless steel,” “granite,” “view” and “landscaped” were found in listings that got a higher sales price than comparable homes.

And there are words you should stay away from, especially “nice.” We think that in the American dialect, you say “nice” if you don’t have anything more to say. And then there are the words that immediately tell a buyer that the house is small: When we analyzed the data, we found that homes described as “charming” averaged 1,487 square feet, “quaint” averaged 1,299 square feet, and “cute” averaged 1,128. All of them were smaller than the average house in our sampling.

The impact of words seems to vary by price tier. For example, “spotless” in a lower-priced house seemed to pay off in a 2 percent bonus in the final price, but it didn’t seem to affect more pricey homes. “Captivating” paid off by 6.5 percent in top-tier homes, but didn’t seem to matter in lower-priced ones.

There are certainly codes in real estate listings that are necessary due to the limited space for words. But, as the article notes some of the words are more precise than others. If someone says they have stainless steel appliances, the potential buyer has a really good idea of what is there. But, other words are much more ambiguous. Just how “new” are some big-ticket items like roofs or flooring or furnaces? The big data of real estate listings allows us to see the patterns tied to these words. Just remember the order for size: cute is small, quaint is slightly larger, and charming slightly bigger still.

If I’m Zillow, is it time to sell this info to select real estate professionals?

Zillow off a median of 8% on home prices; is this a big problem?

Zillow’s CEO recently discussed the error rate of his company’s estimates for home values:

Back to the question posed by O’Donnell: Are Zestimates accurate? And if they’re off the mark, how far off? Zillow CEO Spencer Rascoff answered that they’re “a good starting point” but that nationwide Zestimates have a “median error rate” of about 8%.

Whoa. That sounds high. On a $500,000 house, that would be a $40,000 disparity — a lot of money on the table — and could create problems. But here’s something Rascoff was not asked about: Localized median error rates on Zestimates sometimes far exceed the national median, which raises the odds that sellers and buyers will have conflicts over pricing. Though it’s not prominently featured on the website, at the bottom of Zillow’s home page in small type is the word “Zestimates.” This section provides helpful background information along with valuation error rates by state and county — some of which are stunners.

For example, in New York County — Manhattan — the median valuation error rate is 19.9%. In Brooklyn, it’s 12.9%. In Somerset County, Md., the rate is an astounding 42%. In some rural counties in California, error rates range as high as 26%. In San Francisco it’s 11.6%. With a median home value of $1,000,800 in San Francisco, according to Zillow estimates as of December, a median error rate at this level translates into a price disparity of $116,093.

Thinking from a probabilistic perspective, 8% does not sound bad at all. Consider that the typical scientific study works with a 5% error rate. An eight percent error rate suggests the estimate is right 92% of the time. As the article notes, this error rates differs across regions but each of those have different conditions including more or less sales and different kinds of housing. Thus, in dynamic real estate markets with lots of moving parts including comparables as well as the actions of homeowners and homebuyers, 8% sounds good.

Perhaps the bigger issue is what people do with estimates; they are not 100% guarantees:

So what do you do now that you’ve got the scoop on Zestimate accuracy? Most important, take Rascoff’s advice: Look at them as no more than starting points in pricing discussions with the real authorities on local real estate values — experienced agents and appraisers. Zestimates are hardly gospel — often far from it.

Zillow can be a useful tool but it is based on algorithms using available data.

Zillow back with Trick or Treat ratings for major cities

Zillow is back with its 2014 rankings of “Best Cities for Trick or Treating.” San Francisco, Los Angeles, and Chicago round out the top three. The methodology has not changed much compared to 2013:

We take data seriously here at Zillow, even when it comes to trick or treating. While wealthier neighborhoods are often known for their frightfully sweet harvest on Halloween night, we calculate the Trick-or-Treat Index using a holistic approach with four equally weighted data variables: Zillow Home Value Index, population density, Walk Score® and local crime data from Relocation Essentials. Based on these variables, the index represents cities that will provide the most candy, in the least amount of time, with the fewest safety risks.

In the era of Internet lists, this is a potentially catchy list. The factor of housing values filters out a lot of places with the top 10 dominated by coastal cities with Chicago as the only Midwestern or Southern entry.

Yet, it also seems limited to major cities, not even metropolitan regions, so its scope is limited. Many Americans live in suburbs or smaller big cities and those don’t seem to make the cut here. Perhaps Zillow doesn’t have the same availability of data in these places.

I suspect that people within these cities would not all appreciate it if people took these rankings seriously and in large numbers flocked to the highest-rated neighborhoods just to get better candy more quickly. But, it would certainly be interesting if large numbers did show up…

Zillow starts estimating remodeling costs

Zillow is already known for estimating housing values but just last week they started offering another estimation: what it will cost to remodel.

On Tuesday, Zillow waded into the world of remodeling, by pairing its database of photos of pretty rooms (from its for-sale listings) with the tool that seems to drive most aspects of American life these days, the algorithm.

Thus, the company is attempting to answer the obvious question that dogs glossy magazine layouts and cable TV decorating shows: How much would it cost to do a room like that, anyway?

Remodeling costs are notoriously difficult to generalize about with the public, for a number of reasons…

Undeterred by all that, the website has rolled out Zillow Digs, which has launched with about 6,500 photos of kitchens and master baths. Beyond mere real estate eye candy, though, it also has brought in a team of contractors to offer estimates of the cost of each room’s appliances, finishing materials, labor, etc.

As the article notes, this could be quite a money maker with the number of Americans who remodel their homes. It seems like the algorithm could benefit from getting some data after the remodeling takes place; perhaps a later appraisal or an estimate based on a later sales price. Of course, this would require waiting some time after the remodeling takes place and perhaps there are not enough cases of remodeling within a certain geographic area to make a good estimate.

In the end, it will be interesting to see how many people make use of this new site. Additionally, how many will be willing to make financial decisions based on the estimates from the site?