Home Sales versus Inventory: March 2016

With listed sales of San Francisco homes having “plunged” 23 percent on a year-over-year basis in March and inventory climbing, a trend which shouldn’t catch any plugged-in readers by surprise, Redfin has just deemed the San Francisco housing market as “Overheated.”

Across the greater Bay Area, listed home sales “plummeted” 22 percent last month according to Redfin, dropping on a year-over-year basis for the 21st time in 22 months. And for the first time in four years, the median sale price has fallen on a year-over-year basis, down 1.8 percent to $1.04 million.

In San Francisco proper, the median sale price for a listed home rose 3.5 percent on a year-over-year basis to $1.2 million in March, the smallest year-over-year increase in over a year.

That being said, while movements in the median sale price are a great measure of what’s selling, they’re not necessarily a great measure of appreciation or changes in value and are susceptible to changes in mix (such as a greater share of lower-priced Southern Neighborhood or East Bay homes).

31 thoughts on “San Francisco Housing Market Deemed Overheated by Redfin”
  1. “San Francisco” and “Bay Area” seem to be used pretty loosely and interchangeably here. From one source that tracks MLS sales historically (rereport.com), it appears that for the actual San Francisco city and county, March SFR sales were up YOY in March by 40%, and condo sales were up 13%. I’m not saying things are not crazy, but the actual SF numbers paint a pretty different picture from this report.

    1. While Redfin definitely plays fast and loose with “San Francisco” depending upon the report (local versus national), according to their local report (first link above), listed sales in the city were down 23 percent (versus 22 percent for the “greater Bay Area”).

      We’ll also note that Redfin’s methodology for tracking inventory is flawed as it doesn’t account for San Francisco’s weekly ebb and flow. And according to our counts, they’re greatly underestimating the increase.

    2. The numbers quoted here (-23%, +3.4%) from Redfin ARE for San Francisco proper – albeit one which seems to think “Daly City” is a SF “neighborhood” – so there seems to be a contradiction…you should post a link to your source.

  2. i like how they forgot San Jose, Palo Alto, Fremont and Oakland in their little map.

    bunch of outsiders trying to knock down our RE market.

    haters gonna hate. they mad cuz they cain’t.

  3. In Redfin-speak, the opposite of “Heating Up” is “Overheated,” not the more logical “Cooling Down.” And sales and inventory are “Balanced” when they both increase, but not when they both decrease, then “Low Supply Constraining Sales.”
    When you get your spin from an interested party, expect overheated unbalanced rhetoric.

  4. It’s time to follow Apple-to-Apple resales more diligently. Although anecdotal in nature, they help complete the picture.

    I know that there have been numerous condo sales in 2014-15 that resold in 2016 at a profit. Most notably at the HPshipyard, 4900 3rd st, and a couple surprising ones at 4132 3rd. Not sure this trend will continue, but so far so good.

    [Editor’s Note: Apples-to-Apples]

    1. Yes, on the way up, apples-to-apples doesn’t matter, because bubble economy price increases are illogical anyway. On the way down, apple-to-apple comparisons are crucial, because that way we can inhibit price discovery, and characterize the market as “leveling off” or “correcting” instead of “crashing:” any and all reductions can be attributed to a specific building or neighborhood defect.

      1. How does what you’ve said make any sense?

        Repeat sales indices, such as Case-Shiller, are good stats that essentially aggregate apples-to-apples sales. Combine that with looking at individual apples-to-apples data points and you can form a good idea of what’s going on.

        How does looking at apples-to-apples “inhibit price discovery”? If the same properties are being resold for much less money, then you can say the market is falling. If they get sold for more and more money, then the market’s going up.

        1. Apples-to-apples is a smaller data set, so using it takes longer to identify trends, and using it allows for site-specific rationalizations: “that zip code isn’t the real SF,” or “the kitchen looks dumpy.” You only hear that on the way down when the data need to be spun to disguise the trend.

          You see that kind of rationalizing spin on nearly every reduced-price thread here.

          1. But some of the stats cover too large an area such as the SF MSA. And there aren’t all that many sales in SF proper anyway, especially if you narrow in on certain price ranges and neighborhoods. You really need to look at both.

            People try and spin the aggregate data and they try and spin the data points.

            You can’t have a trend (increase or decline) without data points though and so the apples give you an early warning sign. The aggregate data lags.

            The first few data points of an inflection point are always ambiguous, but the certainty they provide is asymmetric. One or two fallen apples could be the start of a correction or they could be properties with idiosyncratic issues. But if there are no fallen apples, then we know with certainty that we aren’t currently in a correction.

          2. “But if there are no fallen apples, then we know with certainty that we aren’t currently in a correction.”

            Looking at this, I feel I need to add the correction that new construction and flips are part of the market as well. So you need to include comp’s not just apples to apples. But the main point is that a few data points may or may not be outliers, but the lack of data is definitive.

          3. Actually no. Look who is comparing prices that should never be compared, or who apparently don’t know better.

            If they’re not valid comps, they’re not valid stats. This is a fundamental mistake in almost every discussion about the market going on today. A 2 bedroom on Dolores is not a comparable for a 2 bedroom in the Sunset, or in South Beach, or in Daly City.

          4. So Two Beers wants to ignore apples to apples sales and you want to ignore comps? Well then, what’s left? In both cases, sure, someone could spin the data points in a misleading fashion. But the answer to that isn’t to ignore the data, it’s to call people on it when they try to spin the data points.

            If you’re looking at something like the copper market, a ton of copper is the same here as it is in New York as it is in Kansas. And it’s the same now as in 2000 as in 1950. But in the housing market, every house is different and every time is different. I’m not saying that people should use bad data when better data is available. Just that in the housing market you’re never going to get perfect data like you would in a fungible commodities market so you have to make due with whatever sales are actually occurring.

            The main point is that while individual data points may or may not be indicative of a trend, you certainly can’t have a trend without data.

            Mr. Beers seems to expecting a large change in the housing market and my point is just that if this change were to occur, the first indication will be in individual sales. If you can find no fallen apples and no sales out of line of previous comps, then the market isn’t currently dropping.

          5. I don’t think Mr. beers was arguing that one should ignore “apples-to-apples” sales. My understanding of his comment is that such sales are routinely (but unjustifiably) dismissed by realtors or other vested interests with a variety of excuses – dumpy kitchen, busy street, on a hill, non-prime neighborhood, etc. – in an attempt to deny a trend. I.e. not that they should be ignored but that certain parties try to get people to do so.

          6. “I don’t think Mr. beers was arguing that one should ignore “apples-to-apples” sales. ”
            I probably over-summarized here again. My point was just that apples-to-apples have their issues, comps have their issues and stats have their issues. If you use data over a wide area you might get a more accurate number, but then someone says ‘Oh, but area’s X,Y,Z are special you can’t lump them in with the rest of SF’. If you average three months of data you get better stats, but then someone says ‘That data’s three months old!’

          7. Thank you, Bob. Anon misunderstands my point completely, and I don’t have the energy right now to explain myself more clearly than you did.

          8. I’m in line with some of your thinking regarding bubbly pricing for both homes and startups and the ensuing possibility of a correction/popping. And am also skeptical of alot of the spin that gets put out there about stats and startups.

            But sometimes when things are taken too far, they can appear to be fringe views and that can reduce the credibility of core valid points.

            We saw this with the SFBARF group which through some questionable decisions and personnel greatly diminished their potential impact.

  5. Wonder of all wonders…the SF housing market is now squarely correlated with VC funding pace, tech company valuations and general health of the IPO market. All of which are off/down substantially vs March 2015. But despite all the doom and gloom stories detailing “death of the unicorns”, there are still some big private tech companies with huge SF employee counts that will almost definitely go public within the next year or two (Uber, Airbnb and Pinterest lead this group). Anyone who bought around the time FB was going public felt the shift in the market as even mid-level FB employees got closer to multi-million $ liquid positions that allowed them to finally shift from renter to owner. And that for a company located in Menlo Park. Once one or more of the big SF-based unicorns head down the IPO path you’ll see more of this same froth return to the market and push sales activity back up. Might take a year or two…but unlike the first dot.com crash, companies like Uber and Airbnb are massive global profit machines whose valuations might shift a bit down over 12-24 mos, but whose long term viability very few would question.

        1. Look at the Theranos saga for a wonderful example of name brand investors handing over their cash and good names with little to no due diligence. And the play of hyping up your secret blood testing tech to get wishful thinking valuation ratios while juicing revenue by using off the shelf machines. Pure bubble brilliance!

          Tell people you have technology X and revenue $Y and most will assume that $Y is due to X. But with all these profitless companies showing great revenue it’s always a good idea to ask yourself what part of their revenue is actually due to any “secret sauce” that they provide vs what others could do.

          1. theranos is on the margins of biotech, and very few seasoned biotech investos put money in it. When you see TECH VCs leading biotech financing, run away. Peter Thiel does not know biotech, and unfortunately Sean Parkers new bet on immunotherapy will lead to more inefficient spending when lots of money already going to that space. Theranos was a scam, but companies like that represent a small fraction

          2. Just like with housing, both aggregate stats and individual case studies have issues. In the aggregate, you can look up Dr. Ritter’s data showing that recent biotech IPOs are actually worse from a revenue and profit perspective than tech IPOs.
            Because of all the investigative reporting currently being done, looking at Theranos provides an in depth look at a single data point. As with any single data point, just because Theranos did something doesn’t prove that all biotech’s are doing the same thing. But it does show what sort of shady things companies can do and more importantly how easy it is to get away with it. Theranos is 13 years old, announced a partnership with the Cleveland Clinic and Dignity health, partnered with Walgreens, was going to partner with Safeway and was actually running tests on consumers the results of which were affecting treatment decisions. Most people would assume that to get to that level there would have to be a rigorous vetting of the technology. But there obviously was not and if it weren’t for a dogged Wall St Journal reporter who knows how much longer they could have continued. Again, this doesn’t prove that all companies are this shady, but it certainly does proved that many many people had their eyes closed.

          3. “Federal prosecutors have launched a criminal investigation into whether Theranos Inc. misled investors about the state of its technology and operations, according to people familiar with the matter.”

            How timely. A criminal probe and an SEC probe. If these investigations ever get to the point where they become public record, there will be quite the in depth look at this one data point!

    1. Total number of tech IPOs in 2016 so far: nil. Uber CEO has stated he has no interest in IPO. Facebook is the next big short, heading the way of MySpace and Friendster. You can see some of their internal memos with the beginnings of panic if you search online.

  6. Paragon has some very clear and comprehensive numbers comparing Q1 and March 2016 vs. 2015 vs. 2014. Namelink. Median, $/sf, new listings, pendings, closings – some 3-month rolling averages, and some single month. I’m not going to summarize them as they are very straightforward. A fair conclusion to be drawn is that the SF market for houses continues to be white hot, whereas for condos it has “slowed” from white hot to just red hot. We can all still make predictions and read tea leaves, but the present market certainly appears to be a very strong sellers’ market.

    Redfin’s numbers are just too opaque for me to make sense of (and note that economic analysis is what I largely do for a living – doesn’t mean I’m not thick, but I have learned to be suspicious of sources that are not transparent).

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