Big data and AI are transforming the real estate industry, enabling smarter decisions, better customer experiences, and more efficient operations. But how can you leverage big data to improve your AI models in real estate? Here are some best practices to follow.
The first step is to identify and evaluate the data sources that are relevant for your AI models. These can include property listings, market trends, customer behavior, social media, geospatial data, and more. You need to ensure that your data is accurate, complete, and consistent, and that you have the necessary permissions and compliance to use it.
Let's take the example of integrating customer behavior data into a real estate AI model. Assume you're using online activity from a real estate website, where potential buyers list their preferences and interact with property listings. This data can reveal patterns in buyer interest, like a growing preference for home offices post-pandemic. By understanding this data source, including how it's collected and the context behind user interactions, a real estate company can adjust their AI algorithms to highlight properties with dedicated workspaces, potentially increasing the relevance of recommendations and the likelihood of sales in a changing market.
Real estate data is massive and varied, sourced from over 3,000 U.S. counties, each with its own recording methods. Public records data includes property ownership, values, taxes, and debts, all recorded (somewhat ) differently by different counties/states and updated on various schedules. The data quality also varies widely. Preparing this data for AI—cleaning it and combining pieces from different sources—is crucial. It ensures that AI models, which depend on accurate, unified data for training and decision-making, can be effectively developed and used.
An example involves analyzing multiple streams like MLS listings for current market trends, census data for demographic shifts, and local government records for zoning changes. By blending these with financial indicators such as interest rates and employment statistics, you gain a multifaceted perspective. This mix aids in forecasting market movements, assessing property values, and customizing client strategies. For instance, noticing a demographic trend towards younger homebuyers in an area could shift focus to properties suited to first-time buyers. This deep dive into varied data sources is key to informed decision-making in real estate.
Realizing AI's Full Potential in Real Estate: Dive Into Unconventional Data Sources ?? 1. Diverse Data Sources: Look beyond the usual to include climate ??? & development plans ???, revealing factors that sway property values & interest. 2. Climate Insights: Climate data sheds light on property resilience, insurance implications, & allure in weather-sensitive regions ???. 3. Development Foresight: Delve into development plans to gauge future neighborhood shifts, affecting property value over time ??. 4. Buyer Interest Trends: Bring in data on how climate and local changes shape buyer preferences ???. 5. Predictive Edge: Marrying these unique data sources with AI models will make your forecasts significantly better ??.
Adding to the list which have already been discussed I would also include reducing bias and increasing performance: Data Collection and Preparation: 1. Diversify data sources to capture a comprehensive view of the real estate market. 2. Thoroughly clean and preprocess the data to ensure high-quality training data. 3. Create new features from the raw data that are relevant to the specific real estate task.
The next step is to select the tools and frameworks that suit your data and AI needs. You need to consider the volume, variety, velocity, and veracity of your data, as well as the complexity, scalability, and performance of your AI models. You can use cloud-based platforms, open-source libraries, or custom solutions to store, process, analyze, and visualize your data, and to build, train, test, and deploy your AI models.
Data Considerations: Volume: Assess tools capable of handling the volume of real estate data we deal with daily. Variety: Choose frameworks adept at managing diverse data types, from property listings to market trends. Velocity: Prioritize tools capable of handling real-time data updates. Veracity: Ensure selected tools support the accuracy and reliability of our real estate data. 2. AI Model Complexity: Scalability: Opt for frameworks that can scale seamlessly as our AI models evolve. Performance: Select tools known for their performance in handling intricate machine learning algorithms. 3. Flexibility and Adaptability: Cloud-Based Platforms: Leverage the flexibility of cloud platforms for data storage, processing, and AI model deployment.
pick the tools and systems that match what your data and AI tasks require. Think about how much data you have, the different types it comes in, how fast it's generated, and how trustworthy it is. Also, consider how complicated your AI models are, how well they can grow, and how they perform. You can choose from online platforms, free-to-use libraries, or personalized solutions to handle your data, analyze it, and create, teach, test, and put into action your AI models.
As we are building something (in our case an AI tool to predict buuyer from existing data) once you have identified all the data is available to start building you dream project you need to ensure that right tools are available to help you with the build responsible AI model with right objective in this case it means all tools which might be required for data access, data handling, data transfer, model development, model scalability and reusability, model testing, model calibration, model deployment and model usage and adaptability of the AI solutions in development.
Consider a real estate firm aiming to predict housing prices and market trends. They might use Apache Kafka to ingest real-time data from multiple sources. To process and store this data, they could employ the Hadoop ecosystem, particularly HDFS for storage and MapReduce for processing. For example, Hadoop can be used to perform batch processing on historical sales data to identify long-term price trends across different regions. For model development and deployment, cloud platforms are used for their powerful computing resources and scalability. They might leverage TensorFlow within these platforms to build and train complex neural network models that predict pricing based on the nuanced patterns identified in the data processed by Hadoop.
Though in traditional AI, if I can use that term, the cycle of build, train, test and deploy is valid. But world has evolved in last 12 months. There are many foundations models/LLMs exist which are pretrained with vast knowledge of our world and I would simply use one of the LLM work with these curated real estate datasets. First I would feed these data to model as part of the prompt and if that’s not enough then I would consider RAG (retrieval augmented generation) and if that still doesn’t work then I would fine tune a model to support my use case.
The third step is to prepare your data for AI modeling. This involves data preprocessing and feature engineering. Data preprocessing is the process of cleaning, transforming, and standardizing your data to make it suitable for AI modeling. Feature engineering is the process of creating, selecting, and extracting relevant features from your data that can enhance your AI models. You can use techniques such as data integration, data reduction, data normalization, data encoding, feature extraction, feature selection, and feature transformation.
?? Gain a deep understanding of the unique challenges in real estate, like seasonality, pricing fluctuations, and regional variations and Adjust your model accordingly.
For a real estate AI model predicting property prices, data preprocessing might involve standardising property sizes to a common unit and filling missing values in the dataset. Feature engineering could include creating a new feature that calculates the proximity of each property to schools and shopping centers, as these can significantly influence prices. The model can then weigh these engineered features appropriately, leading to more accurate price predictions in different market segments.
Now once you have data and tools required to build you AI engine for predicting potential buyers you need to ensure all the available data is cleaned up and ready for consumption by data scientist which means not only keeping the data in original state but processing and enhancing the data(feature engineering) to add more value to the entire process (its just like when you plan a trip you look at all information you have plus enhance your knowledge by ratings and reviews of transport, place and time of visit to have informed decisions)
Data Scientist @ Wi-Tronix | ASU | BITS Pilani | Ex-Slytek, Drishti, and SemiCab
- Through data preprocessing, outliers can be identified and removed, while feature engineering can derive a composite "amenity score" from various amenities, offering a more nuanced understanding of property value. - Beyond this, the best practices extend to monitoring and updating AI models continuously. - Employing tools such as dashboards and feedback mechanisms ensures real-time tracking of model behavior and facilitates timely updates with new data or improved algorithms. - The synergy between data preparation and model maintenance is essential for staying ahead in the dynamic landscape of real estate AI. #RealEstateAI #BigDataAnalytics #FeatureEngineering #DataPreprocessing #AIModelingBestPractices
The fourth step is to use the appropriate AI techniques and algorithms for your real estate problems. You can use different types of AI techniques, such as machine learning, deep learning, natural language processing, computer vision, and reinforcement learning, depending on your goals and data. You can also use different algorithms, such as regression, classification, clustering, recommendation, sentiment analysis, object detection, and image recognition, depending on your tasks and features.
A real estate company might use regression analysis to predict property prices based on features like location, size, and amenities. Machine learning classification could help in identifying market segments for targeted marketing. NLP models can analyse customer reviews to gauge sentiment about neighborhoods, while computer vision could be used to assess property conditions from images. Each technique and algorithm is chosen to fit specific tasks, optimising the model’s performance for actionable insights in real estate.
Data Scientist @ Wi-Tronix | ASU | BITS Pilani | Ex-Slytek, Drishti, and SemiCab
Whether it's employing machine learning, deep learning, natural language processing, computer vision, or reinforcement learning, the choice depends on your objectives and the nature of your data. Similarly, selecting appropriate algorithms—be it regression, classification, clustering, recommendation systems, sentiment analysis, object detection, or image recognition—aligns with the tasks at hand. For instance, implementing a sentiment analysis algorithm can sift through vast datasets of customer reviews, extracting valuable insights to inform real estate decisions. Employing the right combination of techniques and algorithms ensures a nuanced & effective application of AI in the dynamic real estate landscape. #RealEstateAI #BigDataAnalytics
In real estate price predication ,a combination of techniques can be used together. This is due to the fact that the range of real estate prices can be large and overlapping. The combining clustering with linear regression can help to predict the price. To address the same problem , Another solution is to create pricing categories and turn the real estate pricing to a classification problem and predict a price class ( predefind price range). And there are many more solutions that can be tried out. You should study the limitations and trade offs of each solution ans see what works with your business needs the most.
For solving a problem pertaining to real estate, first and foremost thing to keep in mind is to understand the features properly and their relationships as to how they are dependent on each other and on the output. Usage of clustering techniques plotting heat maps will get the correlation between them. This will help us understand data and which model to precisely use in terms of regression prediction and machine learning algorithms to use. Also hyper-parameter tuning and use of boosting techniques can help a long way. Training a simple artificial neural network may also be handy to get the desired results. For performing sentiment analysis of the customers, NLP techniques such as usage of transformer models will come in handy.
The fifth step is to evaluate and optimize your AI models. You need to measure the performance, accuracy, and reliability of your AI models using various metrics and methods. You also need to identify and address any issues or challenges, such as overfitting, underfitting, bias, variance, and noise. You can use techniques such as cross-validation, regularization, hyperparameter tuning, feature selection, and model selection to optimize your AI models.
As usual, the best practice is to start with something very simple and use it as your baseline. Use historic data from the company's records and compute a simple statistic out of it. Is your algorithm a 0/1 classifier? Then your baseline can be as simple as 50%, like tossing a coin. After that, build a simple and interpretable model and compare it with your baseline. Is it doing better? Then use this model as your new baseline. After that, begin optimizing: your goal is to solve the CASH problem (Combined Algorithm Selection and Hyperparameter Optimization). Try using autoML libraries, like that built by H2O.ai (it's open-source)! It can save you a lot of time you'd be spending with hyperparameter tuning and yield a great model.
When you're rolling out an AI model directly towards customers, it's not only important to run synthetic tests, performed by your own team, but also to evaluate what the real-world impact of your AI model is. Often, benchmarks don't show the reality of what it's like using your AI model in the real world. Evaluate real customer journeys closely, and if needed, use tools, like HumanFirst or others, to aggregate insights across customer segments.
Data Scientist @ Wi-Tronix | ASU | BITS Pilani | Ex-Slytek, Drishti, and SemiCab
- In the realm of leveraging big data for enhanced AI models in real estate, one crucial best practice is to meticulously evaluate and optimize your AI models. - By addressing issues like overfitting, underfitting, bias, variance, and noise, you ensure the robustness of your models. - For instance, in optimizing a predictive model for property valuation, implementing feature selection to identify the most impactful variables and fine-tuning hyperparameters can significantly enhance accuracy, providing more precise and reliable real estate predictions. - Continuous evaluation & optimization are key to staying at the forefront of the dynamic real estate landscape. #RealEstateTech #BigDataAnalytics #AIModels #DataOptimization #TechInRealEstate
Ensure data quality, employ feature engineering for nuanced insights, implement scalable infrastructure, leverage predictive analytics, choose suitable ML algorithms, incorporate geospatial analysis, prioritize data security, ensure model interpretability, embrace continuous learning, and integrate AI insights seamlessly for user experience. This holistic approach enhances AI models in real estate.
The final step is to monitor and update your AI models. You need to track the performance, behavior, and impact of your AI models in real time and over time. You also need to update your AI models with new data, features, algorithms, or techniques as needed. You can use tools such as dashboards, alerts, logs, feedback, and reports to monitor and update your AI models.
Implement robust monitoring to detect model drift or degradation. Real estate is dynamic; models need periodic updates to accommodate shifts in market trends, regulations, or consumer preferences.
In the dynamic real estate sector, regularly monitoring and updating AI models is crucial. This ensures they accurately reflect current market trends and customer needs. By doing so, you can maintain the relevance and efficiency of predictive analytics, pricing models, and customer recommendation systems. Regular updates accommodate new data, adapt to changing market conditions, and integrate advanced algorithms for improved accuracy. This proactive approach not only enhances decision-making but also provides a competitive edge, offering more precise, data-driven insights for buyers, sellers, and investors in the fast-paced real estate market.
Data Scientist @ Wi-Tronix | ASU | BITS Pilani | Ex-Slytek, Drishti, and SemiCab
- Best practices dictate the implementation of robust tracking mechanisms to scrutinize the real-time and long-term performance, behavior, and impact of AI models. Regular updates are essential, incorporating new data, features, algorithms, or techniques as the real estate landscape evolves. - For instance, consider a predictive pricing model that initially relies on historical sales data. To enhance accuracy, the model should be regularly updated with the latest market trends, ensuring it adapts to dynamic conditions. - Leveraging tools like dashboards, alerts, logs, feedback loops, and comprehensive reports facilitate a proactive approach to maintaining the relevancy and effectiveness of AI models in the ever-evolving real estate sector.
Some other best practices, - Implement Predictive Analysis in Real estate by analysing past sales, market trends, and locational data - Merge AI with IoT which embeds AI with smart devices - Drive sustainable development of real estate through AI - AI’s data-driven insights, from rainwater harvesting feasibility, solar consumption potential , pave the path for sustainable real estate practices. - AI utilised in scouting, planning demographic landscapes to soil quality will help drive best practices in real estate adoption and growth!
?Gather diverse, high-quality data from listings, demographics, and economic trends. ?Comply with data privacy laws, anonymizing personal info. ?Focus on relevant features like location, size, and market dynamics. ?Select suitable AI models and use cross-validation to prevent overfitting. ?Integrate with Geographic Information Systems for spatial analysis. ?Regularly update models for market changes. ?Implement user feedback loops for continuous improvement. ?Reduce data biases to ensure fairness. ?Provide clear visualizations for easy data interpretation. ?Test models in real-world scenarios before wide-scale deployment.
The real estate sector is governed by strict government regulations. While AI offers immense potential for value creation within this industry, it's not without its pitfalls, such as inherent biases and privacy issues. It's crucial to source data responsibly and ensure the outcomes of AI models are understandable. Adhering to the pillars of Responsible AI, which include empathy, fairness, transparency, and accountability, is just the starting point. We must also advocate for and establish extra regulatory frameworks to safeguard all AI endeavors in real estate, ensuring ethical and equitable use of technology.
In leveraging big data to improve AI models in real estate, best practices revolve around data quality and model relevance. In my experience, ensuring the accuracy and completeness of the data collected, from property attributes to market trends, is fundamental. One practice I've found particularly beneficial is integrating diverse data sources, such as demographic information and economic indicators, to enhance the model's predictive power. Additionally, tailoring AI models to address specific real estate challenges, like property valuation or market analysis, and continuously updating them with new data helps maintain their relevance and accuracy in a rapidly changing market. #RealEstateAI #BigData #AIModeling
It's very important to understand the data and check all the biases in your data. For example in Boston housing data, there was a bias between rate and ethnicity. To present this type of data, can be awkward and also not ethical. Start with learning insights from data and eliminating unexplainable non generic data points.