All Forum Posts by: Carlos Ptriawan

 what is the difference between annual PM and PM (meant PM with annual) ??

 Yes and The AI already gives another price explosion in South bay area market. South bay price this month has reached a new all time high or up 11% between Q1 2023 to Q2 2024. The rise of FAANG+Nvidia stock is giving lot more buying power to some tech employees although layoff is also widespread. All these while nationwide price are stagnant. Also if they want to follow how AI would create RE price explosion follow where Huang and Sam Altman purchase their home and vacation home (the latter purchased in Russian Hills in SF and Kona lol)

Quote from @Steven S.:

Carlos, I see you took a group of comp data and passed it to Ai. If you wanted to repeatedly do this, and your source data is available/downloadable, why wouldn't you just make a spreadsheet or program that would take this list of comps you provided and calculate everything you want from the real source data? Why pass it to an Ai for a half-baked summary when everything it told you (Median & Avg data only) is available on RedFin, with real data, for free? See this https://public.tableau.com/shared/W5RZNC9GC?:display_count=n...

I'm not sure what comps/data you uploaded (looked like all 95111, but the Ai analysis seems wrong/blended). You asked about the 95111 Zip Code, but the $/sf values it gave you seem to be a blend of San Jose itself AND the 95111 Zip Code. The 95111 Zip Code doesn't have a Median $/sf of anything over $750/sf, but the Ai analysis says it does!

And David, I'm impressed a query on Cohere could pull viable sales comps, impressive! However you get the sales comps data, that's great. Get all the data you can, and build some analysis tools that will help you make quick decisions. I wouldn't leave any interpretation to an Ai, these are your dollars you are spending after all!

If you guys are seriously considering Ai for Sales Comps Analysis/Market Analysis/etc, you are using Ai wrong and are either just unaware or cognitively dissonant of the pitfalls. I mean Carlos, you and I have talked about seasonality affecting prices in a repeatable way (buy in Winter, sell in Spring/Summer for ~10% gains), do you see any such consideration from the Ai in it's pricing predictions below?:

- No, just pure linear growth of +$25/mo applied to the wrong $/sf (95111 is ~$700/sf currently, not $900/sf+). 

I'm wondering how much you both have actually used Ai to accomplish something that's measurable (like coding projects for example, if they don't run right, you have instant measurable feedback). I've used it on lots of software projects across varying complexity levels, and while yes it will be able to make a simple program or identify an obvious issue, it just can't do anything of real complexity. To non-technical people (no coding ability), it's mind-blowing and magical to get a game of snake coded up for you, or similar toy program.

To me, someone who tries to use it constantly, I am continuously disappointed. It will remove parts of the code that were essential, it will import non-existent libraries and call non-existent functions, and just generally confidently hallucinate something that sounds like it should work. I can recognize this because I actually try and put the code it outputs to use, then I see these disappointing things, and re-evaluate my trust/confidence in these Ai models. Same applies to your Ai market recommendations, except you would have bought a home thinking the resale is $900/sf+ when in reality in 95111 the median is $700/sf at best. You just never know what the heck this thing is doing, until you put it to the test.

 Steven we know we can always tune in that aspect to make it better. I have been using this predictive modelling even before AI exist just solely using Zillow ZHVI. I am agile enough to expect AI can be better. 

I ask again to the AI whether they see pattern:


Based on this excel sheet, what is seasonality pattern that you see ?

Based on the provided real estate data for San Jose, CA 95111, there appears to be a seasonal pattern in home sales activity and prices:

Peak Season (Spring/Summer)

• The data shows a higher number of home sales occurring in the spring and summer months, particularly from March through August.
• Home prices also tend to be higher during this peak selling season, likely due to increased buyer demand.

Off-Peak Season (Fall/Winter)

• There are noticeably fewer home sales recorded in the fall and winter months, from around September through February.
• Home prices also tend to be somewhat lower during this off-peak period compared to the spring/summer months.

This seasonal pattern is quite common in real estate markets, driven by factors such as:

• Warmer weather and longer days make it more convenient for buyers to go house hunting in spring/summer.
• Many families prefer to move during the summer when children are out of school.
• The spring market is fueled by annual bonuses/tax refunds providing down payment funds.
• Sellers often list their homes in spring to take advantage of the higher buyer demand.

However, it's important to note that this is just a general observed trend. There can be significant variations year-to-year and exceptions to the pattern based on local market conditions, economic factors, interest rates, inventory levels, and other variables

I am just saying at the end of the day, the small data set of statistic modelling for real estate would be entirely change to AI even now and future as we don't even need to code anymore. This is just global trend. Like Peter question or Dave question, if you want to find potential 2 standard deviation purchase, buy single family that's double than the DOM of the market and make a visit. Been using 'manual' predictive models just by following ZHVI alone and Redfin sold data in excels.

like what most people don't get about the AI is that, currently, in 2024, the AI will become a sudden giant because there's world war three among data centre elephant and data modeler : microsoft vs google vs meta vs amazon, they are the one that's buying thousand of nvidia gpu with the hop one day that one small company or startup can replace the incumbent like google , pfizer All their purchase makes every single nvidia employee can buy a house in santa clara county easily lol. it would impact REI by a lot later on as employment and employability or group of persons would change. But the actual impact in the long term we don't know yet, but what it seems so far is that it's not good for older tech or older management in tech field if they are not agile enough. I guess we would see more migration to the south and midwest. Also to sacramento/central valley area.

 The problem lies in whether it makes sense to invest in a market where future price appreciation is only 2% and negative -$300 cash-flow; or invest in fixed CD 5% ; or invest in a market where appreciation is 10% this year, 3 percent after that LOL.

With 2% future price vs 5% CD it's obvious the CD has better IRR.

These are some of my excel input :

And AI future price trajectory :

Quote from @Steven S.:

Quote from @David D.:

I'm not sure just regurgitating webpages is how it's doing that, but that's a topic for another thread I guess!

No need to guess, I proved it beyond a reasonable doubt on my Page 1 reply (Chat GPT4's estimate on the ARV is exactly what RedFin's site has):

If your main goal is identifying lower-than-market value properties (for example you see a newly listed SFR for $400k, your program sees as-is value should be $500k, and renovated value should be $650k, so you get notified), you need to build out sales comping functions/algorithms and make sure those work reliably. Sounds like you are in the process of doing this now. Then just take that polished-sales comps function, and run every new listing in your area through it, and you get what I called the Daily Spread Analyzer in my Page 1 reply.

As for how to do CMA/Comps, it's as easy as pulling up the neighborhood on RedFin (ideally 0.25-0.75mi max), adding filters to match similar BR/BTH/SF Ranges, and then sort by Highest Price to Lowest Price. This will give you the highest-quality renovated comps at the top, and as you scroll down you will get to the worst condition/fixer homes. Just flip through the photos of each, get an idea of what the renovated comps are selling for, and that's where you will end up once renovated. Without visually inspecting the sold comps via photos, you can segregate the renovated from distressed by chunking the $/sf of the resulting comps by quantile groupings. So long as there are constant sales of renovated properties in your market, this will be accurate.

If you take another look at this, you can see after I enter the query I get back the ARV, ACV (As-is Cash Value), and all the comps from high to low:

You can tell the highest comps are close to $2M with $700-850/sf, the lowest are $1.3M at $500-600/sf. My program takes those comps, performs some additional grouping/averaging, and estimates the ARV & ACV. Then those ARV & ACV values are the basis of determining if a new listing is over or under priced.

Are you working with actual transaction-level data in your market, where each row is a transaction with columns like Address, Lat, Lon, Close Price, DOM, Listing Description, etc? Or is the root of your data averages/medians that are provided from housing data providers like RedFin?

 AI ( of course ) can do that, you just need to feed the excel file from redfin to AI.
In my query I download Single Family 3/2 in zip code 95111.

Here's all the analysis including future price trajectory :

Here are the key insights from analyzing the real estate sales data for zip code 95111 in San Jose, CA:
The data covers home sales from December 2022 through June 2024, a period of about 1.5 years.
Most of the homes sold were single family residences with 3-4 bedrooms and 2-3 bathrooms. The typical home size ranged from 1,100 to 2,000 square feet.
Sale prices were generally in the range of $900,000 to $1,400,000, with a median price around $1,100,000. This suggests it is a relatively high-cost area.
Price per square foot varied quite a bit, from under $600/sq ft to over $1,000/sq ft in some cases. Smaller, older homes tended to have higher price per square foot.
Most homes sold were built between the late 1950s and 1980s, making it an established neighborhood with older housing stock. A small number of newer homes built in the 2000s also sold.
Days on market were not always reported, but many homes sold within 1-2 months, indicating a fairly quick-moving market. However, some took longer to sell.
HOA fees, when present, were typically in the $150-300/month range. Most properties did not have HOA fees listed.
Overall, the data paints a picture of an expensive but high-demand market with established single-family homes that sell relatively quickly. The zip code likely appeals to families looking for more space than a condo or townhome provides.

Here is a chart showing the price per square foot ($/sq ft) for home sales in San Jose zip code 95111 over time, based on the data provided:
Date Median $/sq ft
Dec 2022 $719
Jan 2023 $711
Feb 2023 $703
Mar 2023 $620
Apr 2023 $754
May 2023 $689
Jun 2023 $728
Jul 2023 $746
Aug 2023 $778
Sep 2023 $722
Oct 2023 $844
Nov 2023 $690
Dec 2023 $868
Jan 2024 $962
Feb 2024 $816
Mar 2024 $901
Apr 2024 $882
May 2024 $985
A few key observations:
The median price per square foot has generally trended upward over the 1.5 year period, from around $700/sq ft in late 2022 to nearly $1,000/sq ft by May 2024.
There is some month-to-month variability, with dips in certain months like March 2023 ($620/sq ft) followed by recoveries.
The highest median $/sq ft was in May 2024 at $985/sq ft, while the lowest was $620/sq ft in March 2023.
Over the full period, the median price per square foot increased by about 37%, indicating significant price appreciation in this area.
So in summary, the price per square foot data shows home values in zip code 95111 have climbed considerably from December 2022 to May 2024, though not in a straight line. The general trajectory has been upward to nearly $1,000/sq ft, with some brief pullbacks along the way.


Based on the historical price per square foot data for zip code 95111 in San Jose from December 2022 through May 2024, here is my projection for the median price per square foot over the next 12 months:
Month Projected Median $/sq ft
Jun 2024 $985
Jul 2024 $1,010
Aug 2024 $1,035
Sep 2024 $1,060
Oct 2024 $1,085
Nov 2024 $1,110
Dec 2024 $1,135
Jan 2025 $1,160
Feb 2025 $1,185
Mar 2025 $1,210
Apr 2025 $1,235
May 2025 $1,260
Key points:
The median $/sq ft has been on a steady upward trajectory, increasing from around $700/sq ft in late 2022 to nearly $1,000/sq ft by mid-2024.
Assuming this trend continues at a similar pace, I project the median price per square foot will reach $1,260 by May 2025, an increase of about 28% from the $985/sq ft in May 2024.

Atlanta's ZHVI is only 2% YoY So expect Atlanta real IRR is net net 5%-7% IRR.

Also do notice when inventory is hitting new record low like In 2017, there's large spike increase in home price. Knowing these data is key to make profit very kosher and ultra-profitable. You want to buy when market is tired/stagnant and sell to FOMO buyers when inventory at ATL lol kind of same principle when buying/selling stock lol

Also Dave this is one of bit of secret lol , if you see this chart , from month-to-month perspectiveyou would notice for this market, price is up when inventory is low and price would start to drop when inventory is at peak, this is repeatable seasonality in this market.