Unlock Your Real Estate Alpha. API Product Release 0.4.0 Blog

0.4.0 API Update: Financial Metrics, NYC Price Feed, Las Vegas Price Feed

The Parcl Labs team is proud to announce the release of API version 0.4.0, setting a new industry standard in real estate data analysis. This update introduces two groundbreaking features: the financial metrics endpoint and the addition of New York City and Las Vegas City price feeds. Parcl Labs now offers the only real estate API in the world to offer real estate financial metrics, empowering our users to analyze housing markets with the same rigor as any other investment opportunity.

Introducing the Financials Endpoint

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Parcl Labs is the only real estate data company that can deliver financial metrics for housing market investments

At Parcl Labs, we understand that the housing market is evolving at a rapid pace. Traditional residential real estate datasets and indices depend on lagged and static information, with once-a-month reporting cadences as the norm. This widespread reliance on monthly reporting has, until now, made it impossible to meaningfully calculate crucial financial metrics like volatility for residential real estate. Parcl Labs distinguishes itself as the sole market player that updates data daily, uniquely positioning us to deliver the most accurate and current housing pricing and financial metrics.

We believe that understanding these advanced metrics is becoming increasingly vital for comprehending the dynamics within local housing markets. For example, new institutional players, such as iBuyers, operate with speed, agility, and on a scale that introduces volatility into the markets they engage with. In the past, accurately measuring the impact of their actions has been challenging due to their frequent activities within a single reporting period. As a result, when their activities are eventually recorded, the data appears smoothed over and does not fully represent the true volatility they introduce. With the power of the Parcl Labs API at your fingertips, quantifying the actual impact of these market players is just a single API call away.

How can you access it?

The financials endpoint is accessible at here. If you need to register for an API account, start here with our Quickstart guide.

Overview of the key financial metrics returned by the API endpoint

1. Alpha and Beta: These measures indicate the risk-return characteristics of a real estate investment compared to a benchmark index (in this case, SPY). Alpha represents the excess return of the investment, while Beta measures its sensitivity to market movements. For example, a negative Alpha suggests underperformance, and a negative Beta implies an inverse relationship with the market.
2. Correlation Coefficient: This metric is used to understand the degree and time interval at which housing prices move in relation to a benchmark series (e.g., the 30-year mortgage rate). Often times, rate changes happen prior to fluctuations in the housing market. This can be for several reasons, most notably home buyers can lock in a rate and take 1-2 months to actually close on a home. We check the correlation coefficient against the benchmark series at a 2, 4, 6, and 8 week lag interval. The measurement is between -1 and +1. A high positive correlation (close to +1) indicates that housing prices and the benchmark move together, while a strong negative correlation (close to -1) suggests they move in opposite directions. This information can be useful in assessing how sensitive the housing market is to economic factors, like interest rates.
3. Compound Annual Growth Rate (CAGR): CAGR represents the average annual growth rate of a housing market over a specified period. This metric is helpful in comparing the performance of different markets or investment opportunities.
4. Average Return: This metric indicates the average return on investment for a particular housing market, which can be useful when comparing potential investment opportunities.
5. Sharpe Ratio: This ratio measures the risk-adjusted return of a housing market by dividing the excess return by its volatility. A higher Sharpe Ratio suggests a better risk-return trade-off.
6. Kurtosis: This metric measures the "tailedness" of the return distribution. A higher kurtosis indicates a more significant likelihood of extreme price changes, implying higher potential risk.
7. Annual Volatility: This metric measures the degree of variation in housing prices over a year. Higher volatility suggests greater fluctuations in housing prices, indicating increased uncertainty and potential risk in the market. Conversely, lower volatility signifies more stable housing prices and a relatively consistent market environment.

Use the Financials Endpoint to calculate risk vs. return by housing market

The financial metrics endpoint allows users to easily visualize location-based trends in housing market pricing, volatility, and returns. The chart below showcases the risk vs. return for different MSAs, which clearly demonstrates that Southeast Metros such as Tampa, Miami, and Atlanta are less volatile with better returns (+10%). In contrast, San Francisco is a clear outlier with high volatility and negative returns. These results underscore the theme of regional divergence in housing markets, which Parcl Labs has repeatedly reported in the past few months. You can learn more about regional divergence in this article.

Note that you can reproduce this exact chart using the API recipe available on Parcl Labs documentation site, which is linked here.

Use the Financials Endpoint to understand housing prices relationship to mortgage rates, by market

The correlation coefficient metric offered by the financials endpoint can be used to evaluate how various housing markets respond to changes in interest rates. For example, the Las Vegas graph below demonstrates that housing prices generally fall as interest rates rise.

The chart below demonstrates that the relationship between housing prices and mortgage rates can vary significantly by market. For instance, major California markets such as San Jose, San Francisco, Los Angeles, and San Diego have high price per square foot with a negative correlation to mortgage rates, meaning that as interest rates rise, home prices tend to decrease. In contrast, Florida markets (green dots) appear to have a positive correlation with mortgage rates.

Introducing New York City and Las Vegas City Price Feeds

We are also excited to expand our market coverage with the addition of daily price feeds for New York City and Las Vegas City:

• Las Vegas City's Parcl ID: 5377230
• New York City's Parcl ID: 5372594

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New York City is widely regarded as one of the world's most dynamic and competitive real estate markets. As a global financial and cultural hub, the city is home to a diverse range of properties, from luxury condos to historic brownstones. Analyzing this market using our unique financial metrics can provide valuable insights into the complex economic forces driving the Big Apple's real estate landscape.

Las Vegas City, known for its vibrant entertainment industry, has a rapidly evolving housing market. With strong population growth, increasing demand for housing, and a diverse range of residential properties, Las Vegas City offers a wealth of opportunities for real estate investors. Our exclusive financial metrics allow you to understand the intricacies of this unique market better and make well-informed decisions.

With these new features and expanded market coverage, we aim to provide you with the tools and data you need to make well-informed decisions in the ever-changing world of real estate. Stay tuned for more updates and enhancements in the future!