August 7, 2023

12 min read

Tracking Real Estate Entity Performance Using Parcl Labs Data - Opendoor Case Study


Parcl Labs offers the world's first financial-grade, real-time residential real estate data. Our dataset's differentiated capabilities, including daily updating sales, listing, and rental activity that is directly mapped to the institutional owner, provide users with an unparalleled ability to monitor the performance of both public and private real estate entities in real time.

Parcl Labs’ datasets are particularly effective at tracking Opendoor (OPEN), a public institutional home flipper. Our data bridges the gap between public reporting periods, offering continuous analysis of OPEN's critical industry Key Performance Indicators (KPIs) such as inventory, purchases, sales, revenue per home, hold time, and margin. Moreover, our data provides valuable insights into OPEN’s strategy, revealing which markets they are entering or exiting, pricing and spread dynamics, the types of partnership acquisition and sales channels they utilize, and their perspective on housing price trends.

This case study delves into how Parcl Labs' data can be used to model and understand OPEN’s performance in real-time, offering a more than three-month advantage over traditional real estate data sources. KPIs derived from our datasets are accurate and reliable — our backtests demonstrate that the metrics derived from our datasets strongly correlate with what OPEN publicly reports each quarter. Additionally, Parcl Labs' data provides advanced, proprietary metrics, such as the divergence between list and purchase prices. This allows for the early calculation of expected margins for various home cohorts, a metric unreported and unavailable elsewhere.

Reliable and timely insights derived from Parcl Labs’ real estate data can be directly incorporated into trading strategies, offering the potential to generate alpha.

Executive Summary

  • Utilizing our real-time, entity-aligned datasets, investors gain an immediate understanding of OPEN's real estate activity and performance.
  • These datasets, having undergone rigorous backtesting, empower clients to delve into essential metrics of Opendoor's operations, such as Homes in Inventory, Home Purchases and Sales, Inventory Hold Time, Revenue per Home, and Margin, presenting a blend of public and deeper insights into OPEN's operational health.
  • During the unexpected Q2 2022 and Q2 2023 earnings, our datasets highlighted discrepancies between OPEN's forecasts and street estimates, emphasizing the value in monitoring acquisition and spread metrics essential for predicting OPEN's financial trajectory and strategy adaptations.
  • The OPEN datafeed from Parcl Labs offers in-depth, timely insights into Opendoor's activities, giving traders an edge by understanding the company's performance on a relatively real-time basis, well ahead of its quarterly reports; reach out for a demo. Contact us at to schedule today.


Traditional sources miss key information or are too lagged to be useful in trading strategies

OPEN's financial performance hinges on its capacity to purchase, turn over, and sell homes efficiently. To effectively monitor this process, investors require precise property event data, correctly attributed to the actual owners, and updated daily. Moreover, this data must incorporate reliable pricing information to facilitate an accurate evaluation of OPEN's margin on each transaction.

Prior to Parcl Labs, those tracking OPEN had two options to understand the company’s performance: 1) consume information directly from OPEN’s public website; or 2) use public government records to track OPEN’s sales and purchases. Both approaches come with significant limitations.

Simply relying on metrics derived from OPEN’s website provides a current snapshot, but omits significant context on OPEN’s transactions, most notably the final transaction details. Furthermore, using OPEN’s website as a single source completely overlooks how OPEN is performing relative to its direct competitors (for example, Offerpad), indirect competitors (such as normal homeowners and small-scale flippers), and its position within the broader macro landscape of national and local housing price trends.

While government records provide a more complete picture, they are marked by significant delays and usability challenges. Updates to county records can take weeks to years, making them too lagged for timely analysis. Moreover, data inconsistencies in these records add challenges in interpretation. As a result, traders relying solely on these sources would miss timely insights into OPEN's performance—a nonstarter for sophisticated investment strategies.

Parcl Labs combines the timeliness of real-time sources with richness of government records

Parcl Labs provides real-time, institution-mapped residential real estate data, equipping clients with the ability to continuously monitor the performance of both public real estate entities, such as OPEN.

Our datasets are generated from over 5,000 sources of government transaction records and real-time real estate information. We have developed an enterprise-level ETL (extract, transform, load) process that enables us to handle hundreds of millions of data points from these various sources. We undertake a rigorous data cleaning and harmonization process that includes the following steps:

  • Address Resolution and Property Indexing. Cleaning, de-duplicating, and standardizing property addresses to obtain the highest quality record of each property.
  • Metadata Reconciliation. We conduct a reconciliation process to ensure that the most current information associated with each record is consistent.
  • Owner Mapping. Our system effectively maps parent companies to their subsidiaries and acquisitions, offering a complete perspective of corporate real estate holdings. This process significantly enhances the coverage of owner information. Furthermore, it facilitates the alignment of our datasets with public tickers, such as OPEN.

This approach enables us to create entity-aligned data feeds, empowering financial institutions to analyze OPEN's real estate activities, model its KPIs, and understand its performance in real-time.

Opendoor KPIs and Historical Backtests

Leveraging data from Parcl Labs, clients have the ability to track crucial industry indicators tied to OPEN's operations. This includes not only the KPIs that are publicly reported in filings, but also advanced, non-reported metrics that may offer early insights into the health of OPEN's business.

Homes in Inventory

Homes in Inventory" represents the total number of properties that OPEN holds at the end of a given period. This metric provides investors with insights into the company's purchasing activities, operational efficiency, and exposure to home price trends. The latter is especially critical in an increasingly volatile real estate market.

Parcl Labs' OPEN datafeed enables the direct calculation of the number of properties OPEN holds daily. The chart below presents Parcl Labs’ analysis of OPEN’s cumulative inventory over time. Even from this high-level view, we can gain key insights into OPEN’s perspective on the housing market and broader macro landscape. The company significantly increased its home purchases during the post-2020 home price appreciation rally, and since 2023, it has rapidly pivoted, aiming to accelerate inventory sell-off as quickly as possible.

As the graph below illustrates, Parcl Labs' backtest results closely align with the numbers reported in OPEN's 10Q documents.

The sequential directional trends observed in Parcl Labs' data closely align with those reported by OPEN. This close correlation equips our clients with a real-time, accurate measure of OPEN's inventory holdings.

OPEN reports holdings only at the national level. In contrast, Parcl Labs provides clients with a more granular view, enabling them to assess OPEN's exposure to regional housing trends, entry into new markets, and other local-level dynamics.

Home Purchases and Sales

"Homes Purchased" denotes the quantity of properties that OPEN has acquired within a specified reporting period. Conversely, "Homes Sold" refers to the number of properties that OPEN has successfully sold within the same reporting period. Inventory represents a continuous measure of holdings, while purchases and sales capture specific transactional activities within a defined reporting period.

Purchase metrics illuminate OPEN’s strategic market activity. High volumes can suggest expansion or bullish market outlooks, while low volumes may point to a more conservative approach or pessimistic market views. Sales metrics, on the other hand, indicate the company's ability to liquidate its inventory and generate revenue. The ratio between purchase and sales volumes provides insights into OPEN’s perspective on the market.

Parcl Labs' OPEN datafeed allows for the direct calculation of the firm's purchases and sales at any point in time. The graph below demonstrates Parcl Labs’ analysis of OPEN’s acquisitions and dispositions since 2016.

Purchase and sales counts based on Parcl Labs’ data strongly align with OPEN's reported numbers, particularly when analyzed as a percentage ratio, as demonstrated in the graph below.

OPEN only provides this metric at the national level. In contrast, Parcl Labs empowers clients with more granular data, facilitating real-time analysis of home purchases and sales at any level of geographic detail.

The home purchase and sale metrics should be considered in alignment with other metrics, specifically Opendoor's return on homes. High purchase activity can increase potential revenue, but it also ties up capital and exposes Opendoor to potential risks in a declining market. On the sales front, high volumes do increase revenue, but if these sales are achieved through accelerated turnover or small spreads, it could suggest that Opendoor is pricing its properties in a way that could erode profit margins. Thus, it's crucial to interpret these metrics in the context of overall strategy and market conditions to accurately gauge Opendoor's performance.

Inventory Hold Time

"Hold Time" refers to the duration of time a property is owned or held before it's sold. It begins from the acquisition date of the property and ends on the date of sale. Hold times serve as a significant indicator of OPEN's performance, signaling how effectively the company is managing its inventory exposure. Extended hold times result in increased holding costs. OPEN's financial performance hinges on its ability to flip properties quickly.

While the company doesn't consistently report this KPI in its filings, it often emphasizes short durations between purchase and sale as a demonstration of its efficient business model.

Analyzing OPEN’s hold times is simple using Parcl Labs’ OPEN datafeed. This calculation can be done in real-time, and at any market level. The table below shows OPEN’s hold times have recently increased, particularly in the last two quarters. Phoenix and Las Vegas, traditionally core markets for OPEN, stand as negative outliers, with respective hold times of 283 and 311 days in Q2 2023.

Revenue per Home

Revenue Per Home” quantifies the total revenue generated by the company for each home sold within the reporting period. Revenue per home reflects OPEN’s inventory mix, home price appreciation, and buybox criteria (i.e., what types of properties does OPEN buy). The key inputs effectively include the sales price, the number of homes sold, and OPEN’s service fees.

Parcl Labs' data equips clients with near-perfect visibility into both the exact quantity and sale price of homes sold by OPEN. Thus, we can offer insights into OPEN's total revenue as well as revenue per home. The chart below presents Parcl Labs' analysis of OPEN's home sales amount ($) each quarter vs. with OPEN's reported revenue per home. This visualization underscores that home sales, as anticipated, constitute the majority of OPEN's revenue per home.

The next chart shows that on a percentage change basis, the Parcl Labs’ proxy revenue metric vs. OPEN’s revenue per home metric are almost perfectly aligned.

Home Flip Margin

As demonstrated above, Parcl Labs' data enables real-time understanding of OPEN's home purchases, sales, and the pricing associated with each individual transaction. This granular data allows users to calculate the net profit or loss (”home flip margin”) for each property by subtracting the original purchase price from the final sale price. This metric can be calculated at the individual unit level, market level, and on a national scale.

OPEN presents several key metrics that allow the tracking of its profitability. These include gross profit, adjusted gross profit, and contribution profit. Parcl Labs' home flip margin metric directly corresponds to OPEN's gross profit and adjusted gross profit. OPEN's contribution margin incorporates holding and selling costs. Although Parcl Labs' model does not directly account for these costs, we do understand the factors leading to increased holding and sales costs, as detailed in the inventory hold time section.

The following chart illustrates the correlation between Parcl Labs' home flip margin metric and OPEN's gross and adjusted gross margin metrics:

OPEN’s gross margin - along with any proxy metric for it - is a critical indicator for investors, as it provides insights into the profitability of OPEN's business. As demonstrated in the following section, OPEN's deteriorating margins, especially in core markets like Phoenix, served as early warning signs of earnings misses that led to substantial stock price drops at H2 2022.

Parcl Labs Data Provided Early Insight Into OPEN’s Surprise Quarters

Parcl Labs' datasets provide an advanced signal on OPEN's future performance across key metrics, which can be leveraged to inform trading strategies. To demonstrate the utility of our data, we analyzed two unexpected negative earnings calls for OPEN: Q2 2022 and Q2 2023.

In both of the quarterly earnings call, OPEN’s Q3 guidance came in significantly lower than consensus expectations, and the stock price moved as a result.

Q2, 2022:

OPEN Q3 2022 Guidance on Revenue: $2.2-2.6B

Consensus Q3 2022 Estimate on Revenue: $4.19B

OPEN Q3 2022 Guidance Adjusted EBITDA ($175M)-($125M)

Consensus Q3 2022 Estimate on Adjusted EBITDA: $52.2M

Q2, 2023:

OPEN Q3 Guidance on Revenue: $950-1.0B

Consensus Q3 Estimate on Revenue: $1.36B

OPEN Q3 Guidance Adjusted EBITDA ($60M)-($70M)

Consensus Q3 Estimate on Adjusted EBITDA: ($56.7M)

OPEN projected lower revenue and greater losses than market expectations. Management attributed these negative projections to similar factors: macro, acquisitions, and pricing adjustments. In summary, amidst uncertain home price conditions, OPEN has adopted a strategy of reduced home acquisitions, which, in turn, decreases its resale activity and subsequent revenue. The company's strategy regarding its spread—the difference between purchase and list price, a key determinant of earnings—has evolved in response to current market conditions. With Parcl Labs, these acquisition and spread-related metrics can be tracked in real-time, offering insights into OPEN's strategy and its financial implications ahead of market consensus.

Using Parcl Labs Data to Model Purchase vs. List Spread

Leveraging Parcl Labs' real-time listings data, we can track approximated spread per home based on current asking prices for listed homes. This is achieved by comparing the original purchase price of OPEN homes to their current listing price. Below is a chart depicting OPEN's purchase versus list price trends over the past year.

During Q2 2022, federal interest rate hikes led to a sharp decline in home prices, leaving OPEN with an overpriced inventory. To align this inventory with market rates and minimize losses, the company set more conservative spread goals for that cohort of homes, as evidenced by the notable price reductions. This adjustment is evident on the chart, with a notable drop from 14% to 2% throughout Q2 2022 - Q3 2022. The sharp decline to 2% vividly illustrates the rationale behind OPEN's significant adjustment to its EBITDA guidance, from a range of ($175M)-($125M) against the street’s anticipated $52.2M.

Conversely, in Q1 2023 - Q2 2023, OPEN increased its spread, prioritizing fewer sales with higher margins as a measure to navigate market uncertainty and manage risks. As depicted in the chart (June 2023 - present), Parcl Labs' data indicates that OPEN has more recently (end of Q2 - into Q3) slightly narrowed the gap between its purchase and list prices. This subtle shift aligns the management's remarks during the Q3 earnings call regarding the initiation of a reduced spread pricing strategy. It's plausible that this reduction is a contributing factor to their slightly lower EBITDA projections compared to market consensus for Q3 2023.

Using Parcl Labs Data to Model Acquisitions and Sales

As outlined in the preceding sections, Parcl Labs' data enables real-time insights into OPEN's acquisitions and sales. Typically, OPEN targets a timeframe of approximately ~90-120 from purchase to resale, which aligns with Parcl Labs’ findings around the company’s inventory hold time. This implies that properties acquired in the current quarter will generally be resold and recognized as revenue in the subsequent quarter. Therefore, Parcl Labs’ real-time acquisition data provides users an advantage in forecasting potential resales for upcoming quarters. Sales performance acts as a barometer of how OPEN is faring relative to the guidance it provided in the preceding quarter.

Regarding the Q2 2022 surprise, the chart illustrates that after reaching a peak in June 2022, OPEN slowed its pace of acquisitions in July 2022. The company then executed the steepest MoM decrease in acquisitions in its history throughout the remainder of Q3 2022. At the same time, their sales experienced a downturn — the acquisition-to-sales ratio hit a high of 2:1 in July 2022, illustrating an overall abrupt downscaling of their business. The combined impact of initiating a decrease in acquisitions, a drop in sales volume, and a reduced spread could have been foreseen using Parcl Labs' data before the Q2 2022 earnings call. During this call, the revenue guidance was notably revised downward to $2.2-2.6B, a marked contrast to the Street's Q3 2022 revenue estimate of $4.19B.

In 2023, the chart underscores a pronounced drop in OPEN's acquisitions, with July 2023 recording the company's sparsest acquisition and sales activity in over five years. This slowdown ties back to the spreads. OPEN's targeted margins result in fewer conversions of both sellers and buyers, diminishing the throughput of their business model. Consequently, they adjusted their Q3 2023 revenue guidance to below 1B.

Throughout 2023, OPEN's main strategy has revolved around selling its existing inventory at increased spreads, rather than acquiring new properties. This strategic shift is evidenced by their reduced revenue and diminished cumulative inventory. In Q2 2023, Management has indicated plans to narrow spreads and increase acquisition activity as a strategy to fuel revenue growth in 2024. Parcl Labs' datasets are equipped to detect such strategic changes before any other source, thereby empowering our clients to anticipate shifts in strategy, revenue, and earnings ahead of public disclosures.

How to Access Parcl Labs Data

Parcl Labs delivers an unrivaled solution with its OPEN datafeed. We provide granular, OPEN ticker-aligned datasets that can be utilized to compute all the metrics outlined in this case study, in addition to many other advanced, predictive signals on OPEN’s performance. These insights are not only invaluable, but also provide a potential source of alpha for sophisticated traders invested in OPEN's stock.

Contact us at to schedule a demo today.

Disclaimer: The material contained on this website is provided for educational and informational purposes only, without any express or implied warranty of any kind. The information on this website does not constitute the provision of investment, tax, legal or other professional advice. No reliance may be placed for any purpose on the information and opinions contained herein or their accuracy or completeness, and nothing contained herein may be relied upon in making any investment decision.

Subscribe to our newsletter
Read about our privacy policy.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Share this post