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JohnHokkanen
ParticipantQuestion for RLA….
I agree with your analysis. I have only one question…You say “All properties in San Diego will sell within 30 days if priced properly.”
I read this to mean “Any particular property in San Diego will sell within 30 days if priced properly.”
Since there is a large supply, there are insufficience buyers to buy right now all of the homes even if each day prices were revised to put a portion of the homes in “properly priced” category.
One note: I tend to think about being in top 10% of VALUE rather than price, to reflect inherent differences in features, view, location, etc. The identical floor plan may have different particulars yielding two very different prices. The one with the better value has the better shot at selling, even if it has a higher price. You get what I mean?
JH
ParticipantI didn’t run counts on each of the brackets, but my guess is that the low end (under 750) is the most frequent.
Median market time for Coronado is approx. 119 days (last 90 days, detached homes). 33 homes in dataset.
Yes, these are market times for just homes that sold, which is useful for getting an idea of how long it will take to sell IF it does sell. Months of Inventory is my predictive way of assessing that question because it uses the current inventory number.
Do you think it makes sense to create a “average inventory” computation where we add up the total volume of homes on market each day for a month and divide by days in month. This would help factor in all homes that were listed but cancelled/expired (not relisted). Any comments on whether that has any value?
JH
ParticipantTonight I ran the median market times for North County zips for the past 90 days.
109 days Under $750K
99 days $750K to $1M
182 days Over $1MI also ran a few particular areas for comparison for homes over $1M, last 90 days, Median Days on Market, in descending order. Sales count is in parentheses.
323 Fallbrook/Bonsall (17)
281 Escondido (15)
212 Encinitas (48)
174 Carmel Valley (66)
89 Solana Beach/Del Mar (48)I don’t know quite how to explain the small dip at the 750K-$1M segment, other than to say that it is my experience that the significant break is at $1M, which this sort of shows.
Pretty interesting, huh? A little different than the days on market that you see reported elsewhere.
JH
ParticipantThere should be a direct cause and effect between Months of Inventory and Days on Market (i.e., more inventory –> longer time on market).
Days on Market is a popular statistic used by agents when talking to sellers, so it has some historical usage. Unfortunately, though the use of average days on market was probably pretty accurate for the previous 5 years because sales were fast paced and homes would go in the first listing, that is no longer true, and the statistic calculated by the real estate groups is woefully in error now.
I think Months of Inventory is a really good statistic for many reasons. Inevitably, sellers will ask, “How long will it take to sell?” One could respond, “There are 17 months of inventory, so average Market Time should approach 8.5 months.” (It’s not clear how close it will get to MOI/2 because many homes are withdrawn after 6 months.) What I like about MOI is that it allows future projections of market activity based on current inventory and recent sales. It just seems to me to be a better number. It’s just not as consumer friendly.
Meanwhile Median Days on Market that I’ve calculated is around 120 days for the entire population, but I plan to see how this calc falls out when I break it into above $1M and less than $1M, which is, more or less, where the Months of Inventory jump occurs.
JH
ParticipantThe price brackets used for Months of Inventory are, as I have found, remarkably broad. Up to about 900K, inventory levels usually remain the same. Above 900K, maybe $1M, things start to change. I didn’t think this would be constant across the various zips, but it is.
I a perfect world, I think Months of Inventory ought to be bracketed not by price but by price quartiles. However, most buyers/sellers who are shopping a particular price bracket want to compare between different areas, and they can get confused if you are changing price scales on them.
Anyway, I completely agree with your comments that the same price in two geo areas is different. What the MOI numbers show is that it doesn’t matter below 900-1M…they’re more or less the same. Above $1M, things can vary widely for MOI if you’re looking at 1-1.25 in Escondido vs. Solana Beach.
Anyway, here’s the point I was really trying to make but miscommunicated it…..The average days on market number promulgated by NSDCAR/SDCAR says that stuff is on the market for 60 days. That’s so wrong I can’t stand it. Especially when you look at Months of Inventory that shows, for example, 10 months of Inventory. (If you really believe you have 10 months of inventory, how can it be that market time is 60 days?) The point I was making is that median corrected market time is looking a lot closer to being correlated with Months of Inventory. I have been calculating MOI for a long time and saw this discrepancy (between avg market time and MOI) but was having a hard time understanding what was going on. Now that I’ve computed Median Market Time and found that it’s 120 or more days, that is getting a lot closer to 1/2 of Month of Inventory Time. It will be really interesting to see over the next six months if Median Market Time for upper price brackets really does track the MOI figures (which are as high as 36-48 months). With MOIs in upper brackets as big as they are, one would expect that Median Market Times will grow quite long, though price discounting will probably bring the Median Market Time figure down. Does that make sense or am I just jumbling it up?
John Hokkanen
ParticipantYes, I agree that it is a good project, and I have no doubt that you could come up with a more comprehensive list of data fields than anyone has implemented. However, I think it would be desirable to have an attorney on board who can provide some recommendations re: the licensing issues because any data extracted from the Sandicor database or its derivatives (e.g., Realtor.com, ZipRealty) is probably a derivative work. I don’t think they will allow anyone to publish derivatives without appropriate licensing measures, and the MLSes are very protective of their data. Though it would probably be in the public good to have the MLS data as open source, it isn’t public and the MLSes are not likely to make it so. Ditto even for other folks to like forsalebyowner.com (my guess). Their data is their business.
I’m not saying that you couldn’t bootleg a portion of their data from some sources like public pages of Realtor.com, but that is not likely going to provide you with the information that you need. For example, off market date is not a published piece of data on the listings published on the Internet; its available, more or less, only to realtors using their system. But even if the data is bootlegged and stored in a secure database, that data had better not be made public or it will surely draw the attention of the MLS lawyers.
My model for real estate services is centered around providing objective data and analysis. I would have implemented many more features and ideas, but I cannot do so because I must follow the licensing constraints on their data. You can see the San Diego MLS rules at http://www.Sandicor.com. I don’t know if Riverside publishes their rules.
Though I’m a lawyer (former litigator for the US govt.), I don’t practice anymore and am not a member of the Ca. bar, and so I won’t be of use in obtaining a workable legal opinion on the matter. I would not be surprised if a lawyer who reviews the matter renders an unfavorable opinion on leveraging any of the MLS data. The Dataquick data (i.e., tax data) is good data, but it won’t give you info like off-market date, and my guess is that the Dataquick lawyers will similarly protect their data.
Anyway, these are the reasons why I can’t expose my various datasets to others in their raw form. I’m happy to run analyses and provide summaries of the analysis as secondary summaries is permitted. They’re not my rules; I just have to follow them.
John H.
ParticipantThe more positive unique attributes, the more that the market is insulated from commodity market situations. Consequently, my gut instinct is that, yes, Poway will be insulated to some extent from market conditions that affect other areas where schools have worse scores.
I think you raise an interesting question, which is what is the relationship of school scores to market time? What if we were to graph the data points using elementary school scores as the X axis and Days on Market or Months of Inventory on the Y axis.
As to what segment of the market, I think we might get the clearest picture of a relationship (if there is one), if we looked at the bottom 20% of the market for each area examined. This market is the most resilient to other market effects, and could give good comparison numbers.
Since it is easiest to sort the data by zip code, it would be best to take average school scores for all elementary schools in a zip.
Given how weird the market is right now, I don’t know what we might find. It could be that the least expensive sector in Vista is getting so much pressure due to risk aversion that it performs better than Poway’s least expensive sector. I have no gut instinct as to how the numbers would play out. At least we would be controlling for all the sellers that think they can sell their homes for top dollar. If the numbers came out by saying the best priced homes in a better school district sell, on average, faster than the best priced homes in a worse district, I think you’d have your answer. What do you think?
If you have an idea about what exact correlation might make most sense. Feel free to email me directly at [email protected].
JH
ParticipantI have pieces of what you want because a great deal of my data is normalized and I’ve added a lot to it. For example, I know the geolocation of virtually every property for sale, and that allows me to do cool things like check to see if a home has been relisted because it will have an identical lat/lon.
I have often considered blending my data with my tax roll and foreclosure data (which I have left in separate silos).
Suffice it to say, creating a highly normalized dataset is not an easy task. Humans don’t enter their data consistently, and so many errors occur (wrong zip, misspelled street names, wrong house numbers). You can’t just use something like the tax id, because while that works for homes with separate lots, it won’t work in condo situations. I’ve been working at this for some time, and it’s pretty complicated (not that anyone on this forum couldn’t create a solid set if they had the years and drive to do it).
You must also be careful about licensing issues. You’re going to need to start with someone else’s dataset that has cost them a lot of money to create in the first order. You can’t just expose that data to the world…someone who licenses will need to be its keeper and can assist in how it is utilized. If you don’t, you will surely trigger a justifiable law suit because these are million dollar data sets that they have created even if they are flawed and/or crappy.
You would think that county recorder’s offices would be more high-tech, but the Internet is really only about 10 years old, and many of them just haven’t been able to make their data sets public, and some, like San Diego County, have privacy concerns. Some counties in the US have managed to deal with these issues; in Orange County, FL, you can even go on line and type in someone’s name and pull up their signed deed!
One note on your post. I think everyone who has access to Sandicor MLS has equal access. It may take some doing to extract the data, but it can be done. They don’t expose their geolocation data, so I have paid thousands (tens?) to geolocate my data.
I think the closest thing to what you want is the County data set. That won’t track stuff like homes for sale, including days on market, but it has the core housing data. Dataquick also has stuff on home mortgages attached to each record. Sandicor is exclusively a listing info tool.
On the Sandicor data….The good news is that smart folks that know about some of these errors can leverage them when buying a home. The use of statistics cuts both ways, and we do it all the time for our clients. If you really know what you’re doing and can present rock solid data with an offer, it can get serious attention. No agent, esp. rookies of which there are many, don’t want to look like fools in front of their clients and an esteemed colleague. If you can show that the REAL market time is 89 days and the Months of Inventory is 27 months, you can probably get your low ball offer taken seriously.
Anyway, if anyone has an interesting question that they want me to try to crunch the numbers on my data set to get an answer, please let me know.
John Hokkanen
Hokkanen Real Estate TeamParticipantThanks for the offer. It is now an automated process — but I had to design the algorithms and write the program to do it. I should now be able to run the numbers for any particular zip code(s). I used San Marcos because it was a relatively small set (400) so that it was a manageable process to hand check some of the calculated results.
John Hokkanen
Hokkanen Real Estate Team
http://www.SurfTheTurf.comParticipantI am not too familiar with the lag time issue for the median, so I can’t add anything meaningful in that regard. My experience (anecdotal evidence) was that the market changed in July of last year. That said, there were many, many sales in the fall that continued to set records for the various communities in which they were located. There were some sales that were lower, and some homes were just pulled from the market, but I can honestly say that the market activity that I saw was erratic. There was still considerable buyer confidence in the market, and many people still payed top dollar for homes.
In January and February, sale occurred as is the historical trend. March saw an increase of sales and into April. BUT THEN, the numbers of sales on the records clearly show that buyer confidence/willingness was lacking because sales have declined each month after that initial rally.
I’ve run the numbers, and the number of additional homes on the market each month was not significantly different that the historical trend. However, the drop in sales activity meant that month after month, the inventory has climbed. That had been occurring since January of 2005, but the significant drop in sales gave a hockey stick jump to the inventory this spring. This overnight jump in inventory by 50% was not missed by buyers, and the talk on the street has not stopped since.
So, here’s my point (or what’s my point, maybe)….For the second half of 2005, it was not possible to determine exactly what was going on in general because the data points were a scatter graph. One neighborhood was rallying while another was falling. I can think of one Carlsbad neighborhood in particular whose prices had risen too close to a nearby new construction area, and the result was that all sales stalled (because you could buy a new home for just a bit more), and once they stalled, some sellers got nervous and discounted and that started a slide in that ONE neighborhood. Meanwhile there could be a record price set just a few miles away.
There were many of us who thought the spring rally would burn off some of the excess inventory, and things would remain stable. What is interesting is that some geo/price segments HAVE remained stable and have more inventory than 2 years ago, but it’s not a code red situation. I really like the Months of Inventory calculation better than Median Price because I can see what is happening in different geo/market segments at the same time and have a meaningful comparison.
For example, here’s the Months of Inventory for a couple of markets which are good talking points:
Carlsbad
<600K 8 months of inventory
600-750K 6 months of inventory
750-$1M 7 months of inventory
$1-1.25M 9 months of inventory
>$1.25M 24 months of inventoryVista
<600K 6 months of inventory
600-750K 9 months of inventory
750-$1M 8 months of inventory
$1-1.25M 18 months of inventory
>1.25M 70 months of inventoryEscondido
<600K 8 months of inventory
600-750K 9 months of inventory
750-$1M 13 months of inventory
$1-1.25M 16 months of inventory
>$1.25M 35 months of inventoryIt is pretty clear to me, that of the three, Carlsbad is the healthiest, with it’s high price point at 24 months of inventory. If I told you that there are 52 homes in Escondido’s high price point, but it has had only 1.5 sales per month in that bracket, it becomes pretty obvious about what it takes to be the one-out-of-50 that will go into escrow this month. Meanwhile, look at Vista, with its 6 months of inventory at the low end…that’s 187 homes on the market today with an average sales rate of about 29/month. I think that’s pretty healthy, and I would expect that the low end segment of one of the lower-end
markets to remain healthy even while its high end goes off the charts.Watching these numbers change over time makes it clear to me that median sales price may not be a good indicator simply because it is too coarse a measure. It doesn’t allow one to see into the specific micro-markets that you need to examine for decisioning purposes. If a buyer needs to move here, you can show these numbers, and they help explain, in pretty clear terms, I think, where the short term risks lie in terms of value adjustment. I’m not saying that the low end in Vista might not adjust down a few points, but it is clear to me that the risks in buying in that market are one helluva lot less than the risks of buying in upper end Escondido. I’ve made the point in other posts, but the Months of Inventory indicator shows, pretty clearly, that the Inland markets are in a far more precarious position as we enter the fall than the Coastal markets. I do not believe that the correction that we are about to see will be evenly distributed because the inventory buildup is not evenly distributed.
In summary, I like the median as a way of getting a coarse idea of what’s happening, but I think it should be avoided as any sort of decision-making tool. If you have a better indicator, please tell us about yours.
As a final note, I’d like to say that my median price indicators have shown that Encinitas, strangely enough, has already experienced a correction while the other communities like Escondido have not. The other side of that coin is that Encinitas has one of the healthiest inventory levels compared to its neighbors. So, while some of its neighbors have, for the short term, been able to maintain the median price for sales, they have teed up a situation where the abundance of inventory could result in a sharper correction. I think I prefer the more-stable Encinitas situation because buyer confidence isn’t completley lost as it might be in other areas this fall.
John Hokkanen
ParticipantI think your question pressupposes that a person can wait, and that ability varies pretty dramatically from person to person. Some people have idiosyncratic needs, e.g., one particular elementary school. Within that district, they may have particular needs for floorplan, etc., which narrow the field even further. While the family might prefer to wait for a 20% reduction, their needs may drive them to buy now even understanding that the value may fall because there is a cost to not buying (i.e., not having the right floor plan or not having the kid go to that elementary school). This is especially true if they plan to stay in the home for 10 years.
If you’re looking at some market segments, I wouldn’t buy right now at any price reduction. For example, I wouldn’t buy a $1.4M home in Escondido even if it were 25% below market value because I think we might see a big correction there this fall and that there might be some incredible value opportunities. In other market segments, I think a 10% reduction would be a great win and worth the purchase right now. The distribution of the crash is not going to be felt evenly; some markets are going to “pay” more than others, and, if that is true, then it’s important to know the risk of the impact. I think if you look at a market segment’s Months of Inventory, you will get a decent idea as to how healthy that particular market is. Analyze them be geo/price segment and don’t lump them together because you’ll muddy the results that won’t give you the kind of crisp information that you need to make decisions.
I don’t track Temecula, but I suspect that it is similar to Escondido.
JH
ParticipantI think some geo/price sectors will see a 15-20% reduction. I would be surprised if any geo/price segment suffers close to a 50% loss of value. I think other geo/price segments will adjust from -3 to +3 depending on a host of micro-market factors ranging from elementary schools to view to neighborhood. (For example, now that Carlsbad isn’t building another high school, this has put pressure for some to move to San Diguieto Union High School District. Or the widening of I-5 may make some streets in Cardiff which already have a quite a bit of road noise nearly unbearable.) The challenge is to identify which sector any given home is in and assess the purchase price against the risk & opportunity.
ParticipantI can’t make it this time due to a wedding, but I will try to attend in the future.
I think you’ve assembled a very cool on-line community. I think these things work out best when it involves a niche interest.
ParticipantDoes anyone know of any work that has been done to track market times for FSBOs? I have often thought about trying to do this, but it would be a laborious task to cross reference the FSBO data with the Dataquick sales data. If anyone has this info, that would be great to share some general statistics.
JH
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