How can you use data to identify the best CRE development locations?

Commercial real estate data is an enormously powerful resource that can help identify the best CRE development locations. Property data, such as asset type, location, building size, lot size, number of units, and more, can provide valuable insights about an owner, the properties they own, the financials surrounding those properties, and much more. Knowing the right information opens the door to new business, more lucrative deals, and generally more success overall.

The strategic selection of commercial real estate (CRE) development sites is a data-intensive process. It requires a deep dive into a variety of data points—from occupancy rates and rental trends to demographic patterns. Leveraging data from public records, industry analyses, and online behaviours provides a foundation for forecasting the potential success of various locations. This methodical approach to data allows developers to pinpoint areas with high growth potential, identify market gaps, and assess the long-term viability of investments. By thoroughly analysing and cross-referencing these diverse data sources, professionals can make informed decisions that align with both current market dynamics and future projections.

Um ponto importante a se considerar antes de adquirir ou desenvolver um imóvel comercial é contratar um Estudo de Viabilidade Técnica e Legal (EVTL). Com ele vc terá acesso a todas as informa??es sobre zoneamento, aproveitamento do terreno, área construída, áreas permeáveis, atividades permitidas no local entre outras informa??es importantes. Assim você poderá definir o melhor projeto e entender quais tipos de locatário você poderá atender com seu imóvel.

Quality and quantity are the most important aspects of data sourcing for a CRE development project. Therefore, it is crucial to rely on trusted sources and databases. In many countries, governments and states provide accessible insights into demographics, house prices, ownership records and zoning. Additionally, reports on house price changes and local trends are also often available. Another type of source are real estate agencies and online marketplaces that have access to thousands of transactions. Some of them even provide an API that lets you connect directly to their database for analysis. A more advanced option are online mapping services such as Google Maps or Bing Maps. Both of them offer an API to extract data and information.

When we talk about real estate data so, it generally valuable resource?to gain insights into the property market. Whether you're a real estate investor, developer, or researcher, having access to reliable and comprehensive real estate datasets and databases is crucial.

In the commercial real estate sector, the power of data visualisation is harnessed through advanced tools like Tableau or Power BI to distil complex data into accessible insights. For example, a London-based developer might use GIS mapping to analyze pedestrian flow around potential sites, while interactive dashboards could track the capital's fluctuating office space demands post-Brexit. Heat maps may highlight zones of rapid commercial growth, such as the expansion in Canary Wharf. These visualisations are crucial for investors and stakeholders to quickly understand market trends and make data-backed decisions.

Accurate data collection is the first step, but it's equally crucial to visualise it in a clear, comprehensive manner. The purpose of using data to identify a development location is to convince managers, investors, or clients of the benefits and drawbacks of the site. A powerful and dynamic dashboard will be a fantastic way to handle this. Tableau and PowerBI are two of the most popular applications to achieve this. Maps, charts, graphs, images, videos, texts, and numbers?are among the items to include. The user's experience will be more interactive and tailored if a filter option is used.

Transformez les données brutes en insights visuels. Des cartes interactives aux graphiques dynamiques, la visualisation facilite la compréhension des modèles spatiaux et temporels.

Simplicity Over Complexity: Focus on straightforward visualizations of critical data points: Rent trends over time. Occupancy rates and their fluctuations. Basic demographic data relevant to senior living locations. The Primacy of the Pitch: Stresses that the effectiveness of a site selection proposal hinges more on the narrative and summary that articulate the site's advantages. Suggests that a well-crafted description, highlighting the site's potential based on the data, is more impactful than intricate visualizations. Engaging Stakeholders: Acknowledges the role of data visualization in stakeholder communication but emphasizes that it should support, not overshadow, the persuasive narrative of the site's merits.

Visual data analysis is used to determine good locations in reference to potential developments. We use statistical data to show population growth and emerging areas. The visualized data we share is crucial to get a better understanding of the location of the property the distance between shopping centers, schools, top brand name shops etc. Satellite images show it in detail. As they say, some people are visual learners

Utilisez des modèles statistiques pour prédire les tendances futures. La modélisation permet d'anticiper les évolutions du marché et d'optimiser la rentabilité de vos investissements.

Data modelling in commercial real estate is a meticulous process of projecting the future of potential developments. By leveraging tools like Excel for financial projections and Python for advanced simulations, professionals can examine the viability of sites considering factors like foot traffic, infrastructure development, and economic forecasts. This analysis helps in understanding market trends, evaluating the impact of policy changes, and determining investment potential. A well-crafted model provides strategic foresight, crucial for making informed decisions within the ever-evolving landscape of commercial real estate.

Data modeling in a way of the projection final outcome where contributes previous data from the sources that you must have and connect to the future trends and possible incomes for the final modeling your outcome and last decission in acquiring the property.

In order to maximize potential returns and minimize downside risk. I perform specific calculations and underwrite using analytical formulas to determine best and worst case scenarios. In any commercial property I buy, an in depth analysis is performed on the Financials, profit and loss statements, t3 t12, and other data. The data modeling article is present well.

In the realm of commercial real estate, data validation is a critical discipline that ensures the reliability of analysis. Tools such as R for statistical testing, or QGIS for geographical data integrity, are invaluable for their rigorous audit capabilities. Regular use of these tools for cross-referencing against multiple data points can help identify anomalies or trends that require further investigation. Additionally, incorporating peer reviews and industry benchmarks into the validation process can provide an extra layer of scrutiny, ensuring that the data on which investment decisions are made is both current and robust. These practices are not merely precautionary; they are fundamental to establishing a data-driven culture.

La fiabilité des données est primordiale. établissez des processus robustes de validation pour garantir la précision des informations sur lesquelles vous vous appuyez.

Important step is the Data validation in which you must have real and truthfull previous data income/outcome and as much possible future data from the studies you must take...

Data validation is so important especially in an unstable economy where you cannot really get stats to compare apples for apples or there are not so many transactions happening due to lack of lines of credit. Or at times it's a multi currency economy with a number of policies that affects prices depending on which currency you are transacting on ie are you buying the property using cash or bank transfer.

In commercial real estate, data integration is pivotal, marrying quantitative analysis with human insight. Sophisticated tools and APIs like Salesforce and ArcGIS offer a comprehensive picture, but the real nuance comes from interpreting this data through the lens of human experience. For instance, integrating footfall data with demographic trends can inform not just about potential revenue, but also about the community impact. The most effective CRE professionals use these tools to not only streamline workflows but to also craft developments that resonate with the fabric of the community, ensuring that projects enrich the areas they inhabit.

Persistent Challenge in CRE Data Exchange: Despite advancements, the commercial real estate sector still heavily relies on outdated methods like spreadsheets and PDFs for data transfer during asset transactions. Technology Lag in Data Integration: The industry is lagging in adopting more efficient technologies for data exchange, making the process cumbersome and error-prone. Need for Enhanced Integration Tools: There is a pressing need for more sophisticated tools and platforms to facilitate seamless and secure data integration in CRE. Impact on Decision-Making: This reliance on antiquated systems hinders the ability to make informed decisions based on a holistic view of data from various sources.

Fusionnez différentes sources de données pour obtenir une image complète. L'intégration offre une perspective holistique, réduisant ainsi les risques liés aux décisions basées sur des données fragmentées.

Prioritizing Protection of Stakeholders: Emphasize the critical responsibility of protecting employees and tenants, especially regarding personally identifiable information (PII) like names, addresses, and social security numbers. Elevating Data Ethics in Transactions: Current gap where transactions often involve business professionals instead of data privacy experts, underscoring the need for more specialized expertise in handling sensitive data. Implementing Rigorous Data Ethics Standards: We should advocate for stringent adherence to data ethics and standards to ensure privacy, security, and rights of all data stakeholders.

Respectez les principes éthiques dans la collecte et l'utilisation des données. Assurez-vous que votre démarche est transparente, équitable et respectueuse de la vie privée.

Protection of personal data is necessary in this business. We present our disclosures and or non disclosures to any personnel involved in the company to make sure anything used against us doesn't affect us negatively down the line. It is a matter of ethics and principle.

Data leakage can be so damaging and costly hence the need for it's protection. Because this is something that one cannot control 100% it is therefore important to include data protection clauses in reports, contracts etc

Challenges in Site Selection: There are complexities and uncertainties in current site selection processes in CRE, because it heavily reliance on human judgment and input. Advocating for AI in Site Selection: Maybe we can use AI to revolutionize this process by recommending site usages based on data-driven insights, rather than solely relying on human decision-making.

Investigation in the way where of you get real data from real sources and real studies and do not suggest using AI in any way. AI can be very destractive and liable on AI is something that you would not like. Very risky.

There is too much manual processing and storage of data especially in undeveloped countries. You rarely get historical information online and worse still not so much can be retrieved from archived records. For easy access of data, it would be so helpful if systems can be designed and put in place say at deed office where information such as disposal price, size of property, disposal date, reason for disposal, location etc can be recorded and kept for anyone to access the data when required, students can also benefit a lot from this.