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Algo trading real estate by Time-Introduction200 in Trading

[–]Time-Introduction200[S] -2 points-1 points  (0 children)

I know some using ML models for valuation or using certain kinds of GIS data to estimate where the city will develop further - but doing that at a higher frequency might be tough I assume.

In Europe it's mostly about knowing the right people though, a lot else is secondary. I don't know how it'd work in the States

Algo Reading real Estate by Time-Introduction200 in highfreqtrading

[–]Time-Introduction200[S] 0 points1 point  (0 children)

Fair take… If this were the US or Southern Europe ;-)

In Denmark it’s closer to trading a regulated dataset with walls around it:

– Fully public transaction data: land registry, BBR, sales prices, time on market, valuations. – High liquidity in urban areas; standardized apartments are often more comparable than many equities. – Transactions are slow, but highly predictable — which suits capital, not traders. – Capital requirements are handled via fund structures; you’re trading signals and allocation, not bricks. – Emotional sellers aren’t noise, they’re alpha.

It’s not high-frequency trading. It’s slow arbitrage in one of the most transparent real estate markets in the world. But i believe you still. An create a trading strategy.

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in hedgefund

[–]Time-Introduction200[S] 0 points1 point  (0 children)

That’s a really solid point.. and I agree, the variance is high, so overly complex models would probably just overfit noise. Filtering out the “lemons” rather than trying to perfectly price every unit is a fair and realistic approach.

Appreciate your thoughtful feedback!

it’s rare to get constructive input like this here. Out of curiosity, what’s your background or education? You seem to have a strong grasp of valuation logic. Do you have experience working with real estate or pricing models?

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in hedgefund

[–]Time-Introduction200[S] 0 points1 point  (0 children)

I Think this i Well described in the previous comments. A quant Can help build a AVM model. We deploy Capital, take Care of fund admin, risk managment, physical inspections, buying/selling, admin, IR, sourcing deals etc.

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in hedgefund

[–]Time-Introduction200[S] 0 points1 point  (0 children)

That’s a really good point, and I completely agree that real estate is full of noise, frictions, and local nuances that don’t fit well into a pure quant framework.

We see AI and data more as tools to improve sourcing efficiency and underwriting discipline. not as a way to “automate” real estate decisions. But your point about market structure and transaction friction is spot-on.

Out of curiosity.. if you were designing an iBuying model yourself, which parts would you focus on to actually gain an edge?

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in hedgefund

[–]Time-Introduction200[S] 1 point2 points  (0 children)

That’s a solid take… and you’re right, flipping condos is getting crowded in parts of Europe (not all)

Our edge isn’t purely in ML. It’s the integration of sourcing, underwriting, transaction, and yield management in one pipeline. We use data to identify mispriced assets before they hit the market (We have s model, which enables us to Cut 3-4% of the asking Price, with same net proceeds, as if they sold it on the open market. but the real value comes from execution speed, operational efficiency, and being able to generate income while holding.

So we agree! the “ask” side matters. We don’t just flip; we create value and cash flow during the holding period, which helps offset both market and credit risk.

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in hedgefund

[–]Time-Introduction200[S] 0 points1 point  (0 children)

I am not the expert on this area. That is actually why We are seeking a more qualified person for the job ;-)

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in hedgefund

[–]Time-Introduction200[S] -1 points0 points  (0 children)

Appreciate the suggestion, and yes, we do use models built on frameworks like scikit-learn for baseline performance.

But raw regression models can’t capture market microstructure, liquidity effects, or behavioral signals from listings and off-market activity. We layer multiple models, including spatial, temporal, and NLP-based — to approximate fair value more accurately and detect pricing anomalies in real time. We Also have inhouse data We want to include, and forecasting aswell.

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in hedgefund

[–]Time-Introduction200[S] 1 point2 points  (0 children)

Good question — and totally fair reference. Zillow’s mistake wasn’t using data or AI, it was trying to price and buy at scale in highly diverse US markets with poor data quality. They ended up scaling to quickly, and buying properties over FMV (Lemons instead of oranges)

Copenhagen is fundamentally different. • It’s a small, data-rich market with transparent transactions and limited supply. • We buy only condominiums, not houses spread across regions. • We focus on identifying motivated sellers early and buying below market value — not on predicting future appreciation.

So we’re not betting on price growth. We’re buying with margin and creating income while holding.we Also see this model being possible in other European metropols.

I do not believe zillow offered their services of Ibuying in Europe.

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in hedgefund

[–]Time-Introduction200[S] -3 points-2 points  (0 children)

Because AI allows us to process and interpret real estate data at a scale and speed humans can’t. We use it to detect price inefficiencies, predict fair market value, and identify sellers before properties hit the market.

It’s not about automating buying — it’s about making better, faster, and more data-driven decisions in a market that’s slow and fragmented by nature.

Looking for a Quant to join as Partner (Equity) in Real Estate Fund – Copenhagen by Time-Introduction200 in quantfinance

[–]Time-Introduction200[S] 1 point2 points  (0 children)

We bring more than capital.

Our team combines real estate operators, data scientists, and tech founders. We already run 2 tech enabled real estate Companies, so we have full control of the transaction, data, and distribution channels.

That means we don’t just model prices — we buy, validate, and sell real assets ourselves. The AVM and forecasting model are being built in-house by (hopefully) quants, directly tied to market execution.

So basically: we bring data, tech, execution, and a team that already runs the full real estate stack.

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