What challenges can you overcome when using data analytics in commercial real estate?

CRE data quality is clearly impacted by factors such as incompleteness, outdatedness, and inconsistency, however, in my opinion, the essence of the issue in CRE analytics is just as much about the lack of data. Establishing data governance policies and procedures will create a much healthier data lake for analysis purposes, but given the limitations in key CRE data points available for collection, decisions will always need to rely on both analytics and common sense.

La calidad de los datos es fundamental en el análisis inmobiliario comercial. La mala calidad puede causar resultados erróneos y pérdida de oportunidades. Para superar este desafío, establecer políticas claras de gobernanza de datos y procesos rigurosos de validación y limpieza es crucial. Además, utilizar herramientas avanzadas de análisis para detectar inconsistencias automáticamente puede mejorar la precisión y eficiencia del proceso.

Ensuring data quality in commercial real estate analytics is paramount for reliable decision-making. Challenges arise due to inconsistencies, inaccuracies, and incomplete data. Establishing rigorous data validation processes, employing advanced data cleansing techniques, and regularly auditing data sources are essential. Collaborating with domain experts to verify data accuracy and consistency further enhances data quality assurance efforts, fostering confidence in analytical outcomes.

Data quality includes; -reliable source of the data -most up-to-date data -no N/A values -granular data -data that has been cross-checked and validated

Data analytics in CRE, like any resource, must be verified or its source and the professionals promoting it will be discredited. Don't use it unless you're prepared to explain and defend it.

Algorithms like K-means clustering can identify inconsistencies and redundancies across multiple datasets. Additionally, one can use NLP to parse the unstructured data that has been collected and extract relevant features for integration. This allows for the creation of standardized schemas and ontologies, ensuring all data speaks the same language.

Once you've established your sources of data and verified their accuracy, your next challenge is putting that data together in an efficient and meaningful way. The most cost effective method at a glance is perhaps not so, as it is also the most time and labor intensive. Exporting data in csv files and uploading to spreadsheet software allows for full control and manipulation of the data, but it takes time and skill to set up templates to automate the upload process and to build a dashboard to display and measure that data in a meaningful way. Outsourcing this step is much simpler, and more expensive approach that can save in the long run but it still takes time, energy, and input up front to help map and ensure an accurate process.

When putting different data sets into one, quantifying each data set into a score out of 10 or 100 could help you. This way you can easily combine multiple data and apply the equal weights to be used in your site selection.

The disparate nature of sources like property management systems can create hurdles in achieving a cohesive data ecosystem. Lack of standardization can lead to increased costs & complexity, as well as potentially inaccurate or inconsistent data. You need a strategic approach to data integration. Consolidating these sources into a business intelligence tool creates a single source of truth while still giving access to the same data & metrics you care about. These platforms can also automate/streamline data flow & standardize the data, making the process more efficient & less error-prone.? Focusing on these areas can significantly mitigate challenges of data integration and analytics, enhancing decision-making & operational efficiency.

En este sentido lo aconsejable: Prioriza la selección de plataformas de integración de datos que ofrezcan automatización y optimización del flujo de información, reduciendo así la carga manual y los posibles errores humanos. Asegúrate de que las herramientas elegidas cumplan con altos estándares de seguridad y privacidad de datos, utilizando cifrado y medidas de protección adecuadas para evitar vulnerabilidades. Implementa protocolos de datos estandarizados y compatibles para facilitar la interoperabilidad entre sistemas y garantizar un intercambio fluido y seguro de información entre diferentes plataformas y fuentes de datos.

Not having a bias is one of the most important thing when you conduct a data analysis. Always analyze strengths and weaknesses of certain target location, and try to come up with a solution to mitigate risks for some weaknesses of a market.

Para superar el desafío del análisis de datos en CRE: Fomenta una cultura organizacional basada en datos, donde se valore la toma de decisiones fundamentada en información. Esto puede incluir la implementación de programas de capacitación en análisis de datos y la promoción de la alfabetización en datos entre los miembros del equipo. Invierte en plataformas y herramientas de análisis de datos que simplifiquen el proceso y proporcionen capacidades avanzadas, como visualización de datos, inteligencia artificial y aprendizaje automático. Esto puede ayudar a hacer frente al volumen y la complejidad de los datos, permitiendo generar insights significativos de manera más eficiente.

Just because you have the best quants, Excel?, and Argus? jockeys on the payroll, and proprietary VBA and other custom softwares, doesn't mean all of the risk is removed. Also, at this level, making a particular investment in a property can be just as easily justified as NOT making one - as "paralysis by analysis" can be a real phenomenon. Whether it's the above, or massive AI implementation, technology will never remove the human element of determining the ultimate validity in making an investment in a property.

Big data is driving industrial real estate decisions. It is important as a broker to understand the nuances of the data. Data is ubiquitous, knowing what to do with these figures, and where there are holes in the numbers, are where a broker can add real value.

The data helps drive the accuracy of the solution and more importantly helps align the teams for inclusive engagement. Incorporating shared enterprise data-sources and predictive analytics coupled with historical data has enabled me to align the stakeholders for better outcomes. Expected outcomes and results, improved forecasts and greater satisfaction were the key takeaways from improved data sharing.

Para superar el desafío de comunicar datos en CRE: Adaptar el enfoque de comunicación a la audiencia y objetivo específicos, utilizando lenguaje y formatos comprensibles y relevantes para cada grupo de interesados. Emplear técnicas de narración de datos para contextualizar la información, contar historias convincentes y destacar insights clave que resuenen con la audiencia. Utilizar una variedad de canales y medios de comunicación, como informes visuales, paneles interactivos y presentaciones dinámicas, para maximizar el impacto y la accesibilidad de los datos presentados.

I think that here, it's all about the "KISS Principle" - Keep It Simple Stupid - but while also knowing your audience and reading a room. You're not going to give a mom-n-pop or small investment group the same content, context, and level of detail at you would a multi-billion dollar global REIT. The story one tells should combine the appropriate level of reporting intel and metrics, but in a value-add fashion whereby your client is far better informed - but not overwhelmed - because of the work you did, and how you did it. That's the best easy to instill client confidence and establish trust.

Explore many different types of data to get ahead of your competitors. Satellite data, real time data, and urban planning research data are all good examples of innovative approach to data analysis.

Para superar el desafío de innovar con datos en CRE: Adopta marcos de innovación basados en datos, como el pensamiento de dise?o o la metodología ágil, para guiar desde la ideación hasta la implementación de nuevas ideas y soluciones. Fomenta una cultura organizacional de experimentación, colaboración y aprendizaje que estimule la creatividad y la agilidad en la búsqueda de nuevas propuestas de valor. Utiliza plataformas y herramientas de análisis de datos para respaldar los esfuerzos de innovación, permitiendo la exploración de datos de manera eficiente y la identificación de oportunidades para la creación de nuevos productos, servicios y soluciones en el sector inmobiliario comercial.

Properly tracking the market with PhD student intensity allows you to hush all the crap noise you hear from social media and buying groups about market conditions that just aren't happening in the way the masses want you to believe they are. Focus on the data and you'll be way ahead of the crowd that just regurgitates info they read from unverified sources. Studying actual stats in my market brings me peace (or scares the hell out of me) depending on what the data is saying.

One thing serving me over the last 35 years was using the competition to stay ahead of the competition. Here's what I mean.... Early in my career (before PDFs were a thing, and yes - I know I'm old), I kept a banker's box next to my desk. Any time I saw something that resonated - a cash flow, a flyer, an OM, a property tax analysis - I'd photocopy it, put it in that banker's box, and every so often go through it and improve it, turning it into my own. Now - with technology and the speed of information - not only is this easier to do than ever, but there is exponentially more content in circulation. The potential for improvement and individuality here is limitless. Start yourself a "digital banker's box" and thank me later.

Another aspect to consider is the timeliness of data. In CRE, market conditions can change rapidly, making this factor crucial. The value of data is significantly diminished if it's outdated, as decisions based on such data may lead to missed opportunities or misinformed strategies. So, real-time or near-real-time data processing is essential for making agile decisions. This can include updates on market prices, occupancy rates, or changes in local regulations that could affect property values and investment decisions. Timely data also allows for proactive decision-making. One example would be in property management and being able to address maintenance issues before they escalate or adjusting rental rates in response to market shifts.

Data should not be viewed as an end all be all solution. Its additional context that brings value to help make an informed decisions. I will continue to maintain nothing beats physically spending time in a trade area to observe first hand and talk to businesses. Boots on the ground! Again though, it is all about having as much information as possible for informed decisions.