Skip to content
×
PRO
Pro Members Get Full Access!
Get off the sidelines and take action in real estate investing with BiggerPockets Pro. Our comprehensive suite of tools and resources minimize mistakes, support informed decisions, and propel you to success.
Advanced networking features
Market and Deal Finder tools
Property analysis calculators
Landlord Command Center
$0
TODAY
$69.00/month when billed monthly.
$32.50/month when billed annually.
7 day free trial. Cancel anytime
Already a Pro Member? Sign in here
Pick markets, find deals, analyze and manage properties. Try BiggerPockets PRO.
x
All Forum Categories
All Forum Categories
Followed Discussions
Followed Categories
Followed People
Followed Locations
Market News & Data
General Info
Real Estate Strategies
Landlording & Rental Properties
Real Estate Professionals
Financial, Tax, & Legal
Real Estate Classifieds
Reviews & Feedback

All Forum Posts by: Carlos Ptriawan

Carlos Ptriawan has started 84 posts and replied 7089 times.

Quote from @David D.:

These are two different but related strategies it sounds like. Steven is using an ARV model that's homegrown, and ChatGPT is using Redfin's ARV model, probably with its own intuitions about repair values baked in there too.

These are still different from my approach though, I think? I'm assuming Steven is also using a small dataset of Comps. I've been skeptical of Comps since first hearing about them. If there's only a few comps you're using, there's naturally going to be a lot of error in your valuation model, so you're at greater risk of buying a home that really isn't going to net you much of anything. 

That's why I like this traditional ML approach, we leverage lots of data to inform our decision about which house is seriously "undervalued"

I have a couple of questions about both of your approaches though, will be interesting to compare them!

1. In Steven's approach, is the strategy to choose a house that has a high ARV but low current price, or to just select based on the ARV? 
2. Have both of you found that these approaches are accurate in your own investments?
3. Do you think an approach with floorplans (which I just found out Redfin has), would be more effective? 
4. I'm curious about whether you've tried to model the effects of remodeling alone. For instance, let's say I remodel the home completely, have a completely different look inside of it than it had prior, as opposed to the smallest possible remodel. There is presumably a sweet spot in between that has the best "value" on average. Have you explored this? 


Floorplan is very important.

Also David have you try ever to use Privy or Pellego. Both software can guess-estimate the rehab cost from the pic. I check the inspection and picture, and create a range of estimates for rehab budget, for example 25k-50k or 50k-75k based on which house that needs to be remodelled. Then add 10-20k for additional unexpected budgeting. 

For number 2. Yes. Every single time my prediction is deadly accurate. I even know when is best time to sell (Second week of February).

Most home, when needs to be fully remodelled, would cost me around 80k-100k. So for something that low comp is 800k and high comp is about 900k. You want to buy in the price of 600-680k. One way to make remodelling works is when you buy home that has a larger lot, then your creativity could kick in to create value.

Also if you do rehab in good market, you better rent it out for two years before selling so you can catch rent, reduce tax and make more money. If you can add additional room and/or kitchen the value of the home is higher.

Why I said this data is becoming too easy is because, if you chase in good market (DOM < 10/15), market is efficient so much so that the best house to buy is usually home with price reduction or having DOM double than the normal. If I visited only these house I can still find good deal.

Quote from @Steven S.:

That's the same info I could find on the RedFin link for the property, it's summary is great, but it's just nothing like what the guy here is trying to do


 It's the same at the end. To investigate market you just need Sale List of trends such as this:
https://www.redfin.com/neighborhood/10958/CA/San-Jose/East-S... 

Then if one wants to be very specific, do a query in Redfin of sold homes. Download it and put the Excel file, create a chart. Use the future appreciation by using ZHVI. Also since there are 30-40 homes only, it's easy to create mean or average statistics. Software like Privy or yours (like we discussed a few months ago) can detect if the house is flipped or not, based on the picture or recent data.

I think something like Tableau now should have something like automatic prediction when you feed your data to them. For real estate this is just too easy as datasets are too small.

Quote from @Jacob Stevenson:

What is the key to finding a great property management company?  How do you vet them?  


 I know a few good PM.

The secret: passionate owners !!!!!!
small pop and mom owner!

Quote from @Jay Hinrichs:
Quote from @Carlos Ptriawan:

These are the cities if one wants to do land acquisition based on data center/AI conviction:

Northern Virginia

  • Largest data center market in the world with over 300 data centers and nearly 4,000 MW of power
  • Dense fiber connectivity and proximity to subsea cables
  • Relatively low risk of natural disasters
  • Competitive electricity rates and tax incentives

Dallas, Texas

  • Third largest data center market in the U.S. with over 150 data centers
  • Central location with excellent connectivity
  • Low electricity rates and ample available land
  • Minimal risk of natural disasters

Silicon Valley, California

  • Hub of tech innovation with nearly 160 data centers
  • Robust fiber connectivity and proximity to leading tech companies
  • High electricity and real estate costs, but still in high demand

Chicago, Illinois

  • Over 110 data centers with 805 MW of power
  • Central location serving as connectivity gateway between coasts
  • Reliable, mostly underground power grid
  • Lower cost of living than coastal cities

Phoenix, Arizona

  • Rapidly growing market with over 100 data centers and 1,380 MW of power
  • Affordable power and real estate costs
  • Robust connectivity and minimal natural disaster risk
  • Access to renewable energy sources

once up an running though data centers dont employee very many people..

They don't, but it's one of the businesses inside the real estate that's extremely profitable and has a lot of growth for the owner. Datacenter's CAGR is about 20%. Compare that to the Multifamily biz which only has CAGR of 1%. Their business growth is not impacted by employment or interest rates or any changes in economic policy. Our Oregon DC only employed a quarter of the soccer team lol. However, due to this future AI explosion, we expect more DC to be built on-premise or over the cloud. For example, a lot of large S&P has HQs in Minnesota, then expect each of those companies has started to build one inside the state.

they know about kitchen renovation too LOL :

The property value of 22757 Charlemont Pl in Woodland Hills, CA has increased significantly over the years.The key details on the property value changes are:

  • In August 2016, the property was sold for $941,000, which translates to around $345 per square foot.
  • In June 2024, the property was sold again for $1,857,500, about 97% higher than the 2016 sale price.
  • The 2024 sale price of $1,857,500 for the 2,730 square foot home equates to approximately $680 per square foot.

So in the span of around 8 years from 2016 to 2024, the property value nearly doubled, increasing from $941,000 to $1,857,500. This significant increase reflects the rapidly appreciating real estate values in the Woodland Hills area of Los Angeles over this period.The search results also note that the property underwent some renovations, such as a remodeled kitchen with high-end appliances, which likely contributed to the higher selling price in 2024 compared to 2016, , the property value of 22757 Charlemont Pl in Woodland Hills appears to be higher than many similar homes in the surrounding area, but is generally in line with the rapidly appreciating real estate market in this neighborhood.Here are some key comparisons:

  • The sale price of $1,857,500 for this 2,730 sq ft, 5 bedroom, 4 bathroom home in June 2024 equates to around $680 per square foot.
  • The median sale price per square foot for homes in the Woodland Hills area in April 2024 was $638, up 9.7% from the previous year according to Redfin. So 22757 Charlemont Pl sold at a premium over the median price per sq ft.
  • However, Redfin notes that "hot homes" in Woodland Hills can sell for around 4% above list price and go pending in around 15 days. 22757 Charlemont Pl sold for about 2% below its $1.9 million list price after being on the market for a bit longer at 30 days.
  • Zillow data shows the typical home value in Woodland Hills in April 2024 was $1,257,932, up 9.6% year-over-year. At $1.86 million, 22757 Charlemont Pl sold at a premium to this typical value.
Quote from @Don Konipol:
Quote from @Chris Seveney:

@Drew Sygit

Seeing a lot of posts here and on other forums about people buying real estate or making a quick buck, but that’s like someone bragging that they bought a stock that went up the first week. Let’s see where you are at in 3-5 years.


 "Never confuse genius with a bull market"


Don seriously you need to write a book "my anti thesis of what Biggerpocket tell you" ;-)
I wish everyone write like you.

Quote from @Corbin H.:

I had a sewer line collapse at my rental. The city came out and said it isn't city responsibility. Insurance came out and said it isn't covered. Plumbing company doesn't have a payment plan. All in they want $20k that I don't have. I've had this rental for 10 years and never had a massive repair like this. Do I have any other options that maybe I don't know about? 


 You can also sell and inform this condition to the buyer that they have to fix the sewer. Have you try other plumber ? I checked for  home for seller with condition to repair sewer , the contract sometimes they ask in the range of 6k-15k only. I don't know why yours are expensive.

Nice Steven....

And AI can give comps very fast as well these days lol :

Based on the recent home sales data for Woodland Hills, CA, it's difficult to determine if 22757 Charlemont Pl specifically has a good ARV (after repair value) without more details on the property's condition, size, and features compared to other recently sold homes. However, we can make some general observations:
The median sale price for homes in Woodland Hills was around $1.2-1.3 million in recent months, with prices trending up 4-7% year-over-year.
Homes are selling relatively quickly, with an average days on market of 35-44 days. Hot homes can sell even faster, in around 15-24 days.
The price per square foot averages around $640.
Example recent sales include:
$1,140,000 for a 4 bed, 3 bath, 2,228 sqft home on Oxnard St
$1,300,000 for a 3 bed, 2 bath, 1,936 sqft home on Hatteras St
$1,612,500 for a 3 bed, 2 bath, 2,161 sqft home on Oakdale Ave

Quote from @Bruce Lynn:

 drivers.   The other concern is that I feel like fuel prices are artificially low.  Oil is $80 right now and some people predict $200.  Many of those remote buyers will not be able to afford to fill up monster trucks and SUVs to make that commute to work.

I'll be interested to see how zoning changes/increased density actually works.
When I lived in CA almost all my staff lived with family.  Maybe 1 guy out of 80 had his own small 100 year old apartment.  Most had mom and dad, and 2-3 kids and their families and grandkids living in the same house.  I guess 1 family per bedroom.  I'm starting to see more of that here, but not pervasive yet.  Instead of 3 families renting their own apartment, they'll rent a bigger house and move intogether.   I think we will see more and more of that.  Maybe that is the opportunity for Gen Z to make some money house hacking.


 One thing that I do notice as when Bay Area expands, although the distance from a new home to the office is about 1 to 2 hours ; many of them use EVs like Tesla.

As Tesla is now cheaper than Camry/Corolla I don't know why oil $80 or $800 still a factor. Also supercharger is everywhere now just charging for 10 minutes and you get lot of juices. Combined with FSD an hour drive to office two or three times a week is nothing.

Also most tech company only really work Tuesday to Thursday. I hardly see any traffic on Monday lol.

Quote from @David D.:
Quote from @Carlos Ptriawan:
Quote from @David D.:

I am curious if anyone has employed the simple strategy I was thinking of for investing in any kind of property:

1. Use something like ATTOM API to get historical sales snapshots, or just all the historical sales of the property class for a metropolitan area. 
2. Train a regression model using square footage, bathroom count, or more advanced features to predict the price of the sales. 
3. Get the error of your predicted housing price (if you're using price per square foot, convert to housing price) down to $10K-$40K or so. 
4. Find properties with characteristics that predict they should be selling for 2 standard deviations above what their actual price/value is (or basically just properties that are selling for way below what the model predicts they should sell for).

The challenge here is of course step 3. I just started experimenting with Downtown Detroit with ATTOM's "sales snapshots" and I haven't gotten that kind of performance yet. However, I'm sure many people have, especially if they have images or floor plans of the houses/other features and lots of data. 

I apologize if this is all just fairly standard financial modeling. Very key thing here, I don't have a finance/economics background, and am just getting started in this area, but this struck me as a good strategy to use. Also looking at neighborhood trends. My worry is that this wouldn't work because any undervalued property has its price for a "reason", e.g. it is already at its equilibrium price by the time you've determined that its "undervalued", and in fact you won't be able to flip it for whatever reason for a huge profit.

I don't understand how that could be the case though. What seems more intuitive for me is that these are the homes that just happen to have so far been ignored by other investors, or else passed over for better deals, since a small group of investors cannot snatch up all of the deals (otherwise why would this forum exist haha).

Another argument might be that you can never tell when a neighborhood is going to crash in its property values, or when people will migrate to a nearby one that is flourishing. If this were the case, then your model wouldn't be effective anyway. Presumably you can also protect against this with the aforementioned neighborhood growth trends.



 The predictive model is actually very easy to create.
1. You sorted out market and zip code that has positive appreciation
2. You sort everything based on DOM
3. Make time interval, lets say within 3 years period, you check a city that has reducing DOM within 3 years
4. Redfin all these information
5. Results are that Detroit is part of that city. Not surprisingly, ZHVI also has very similar data result
6. You sorted again within the city of Detroit, what home has the lowest gap to the mean
7. There you go, submit your bid

But Privy already does that and few other software too. But honestly you can do the same by just downloading from Redfin data periodically. Been doing that for over ten years now lol


 A Redfin export seems pretty useful, but don't they usually use very aggregated data? I'm not sure which export to use as I didn't find historical sales. 

These other aspects of the strategy seem great, really reduces the chances the neighborhood is just going to tank in value or something I would think, though I suspect fitting a curve as opposed to just using the mean may be a bit more effective and less risky. I'll try it out! Would you feel comfortable sharing your approximate "loss rate"? That is, the rough percentage of the investments you make that end up costing you? 


 you can download recent home sales using Redfin, for example : query 3/1 SF in three Detroit zip code. Put it to excel file. Upload it to tableau for nice graphic so on.

I've been investing in last ten years using prediction and it's pretty accurate, it's not hard to made prediction based on seasonality and historical chart. Feb to July usually has the strongest activity and then flat after September.

Problem with real estate is that ........ it is too easy, if you download the data that you interested to look for, you only have 30-50 homes and make the plot or prediction based on this input is again.....very easy.

Also Axos has good data as well using Listing to Sold Ratio listing.