X Grok-2 Is Available Through X-Premium

[RCA]

If you have X Premium, you can access the Grok-2 interface under "Grok" in the side window.

It is reported by some software engineers to now be the best for generating code, - although Claude is better in some respects.

It is both reported by others, advertised by X and in my view as well, better at reasoning than ChatGPT or Claude.

Here's an example query:

Me: Do you know anything about MARS regression for appraisal of real estate

"Yes, MARS (Multivariate Adaptive Regression Splines) is a type of regression analysis that has applications in various fields, including real estate appraisal. Here's an overview of how MARS can be used for real estate appraisal:

What is MARS?

MARS is a non-parametric regression technique introduced by Jerome Friedman in 1991. It is designed to model complex, non-linear relationships between a set of predictor variables and a response variable. Here’s how it works in the context of real estate:

Non-Linear Modeling: Unlike traditional linear regression, MARS can capture non-linear relationships through the use of piecewise linear basis functions. This is particularly useful in real estate where property values might not increase linearly with attributes like square footage or age of the property.
Adaptive: MARS is adaptive in that it builds the model by selecting the best points (knots) where the function changes its behavior. These knots define the segments of the splines.
Interactions: It can automatically include interactions between variables, which is handy when considering how different features of a property might interact to affect its value (e.g., the effect of an additional bathroom might depend on the number of bedrooms).


Application in Real Estate Appraisal:

Property Valuation:
MARS can be used to predict the market value of properties based on various characteristics like location, size, number of rooms, age, amenities, etc. It allows for the creation of a model that might capture nuances in the market better than linear models.
Feature Importance:
It helps in identifying which features of a property most significantly influence its price. This can be useful for appraisers to focus on key attributes when valuing properties or for developers to know which features to emphasize in new constructions.
Handling Complex Data Structures:
Real estate data often includes both numerical data (like square footage, age of the property) and categorical data (like neighborhood, style of the home). MARS can handle these mixed data types effectively.
Adjustments for Market Trends:
MARS models can be adjusted or extended to account for market trends, seasonal effects, or economic conditions, making them dynamic tools for valuation over time.
Comparative Analysis:
By creating models for different neighborhoods or property types, MARS can aid in comparative market analysis by providing a structured way to compare properties.


Advantages for Real Estate Appraisal:

Flexibility: MARS can model the non-linear and interaction effects common in real estate data.
Feature Selection: Automatically selects significant predictors, which can reduce the complexity of the model.
Interpretability: Despite being a non-linear model, the output can still be interpreted, with each segment of the model having a direct, albeit piecewise linear, relationship with the predictors.


Considerations:

Data Requirements: Like any statistical method, MARS needs a good amount of data to perform well, especially to identify the optimal knots.
Overfitting: There's a risk of overfitting with MARS if not careful, especially with smaller datasets. Regularization or careful model selection can mitigate this.
Software: While there are implementations of MARS in R (earth package), Python (like py-earth), and other environments, it's not as universally supported as simpler models like linear regression.


Conclusion:
MARS can be a powerful tool for real estate appraisal due to its ability to handle complex, non-linear relationships in property valuation data. It provides an advanced method for predicting property values, understanding market dynamics, and making informed decisions in property assessment. However, its complexity might require more expertise and data than simpler models, but the insights gained can be worth the investment for sophisticated appraisal needs."
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ME: ]Do you know of instances where MARS is actually used for real estate appraisal.


Grok-2: "While I don't have access to real-time or specific proprietary cases, MARS (Multivariate Adaptive Regression Splines) has indeed been explored and implemented in real estate valuation in various academic studies and potentially in practice by sophisticated appraisal firms or real estate analytics companies. Here are some general insights and examples where MARS might be or has been used for real estate appraisal:

Academic Research:

Valuation Accuracy:
Studies like the one by Aminian et al. (2012) in the Journal of Real Estate Research have compared MARS with other methods like hedonic pricing models for predicting residential property prices. They found that MARS could offer improved accuracy over traditional models in certain scenarios.
Mass Appraisal:
MARS has been considered for mass appraisal models in some research. For example, Peterson & Flanagan published a study where MARS was used for assessing property values for tax purposes, demonstrating its capability in handling large datasets with complex interactions.
Market Segmentation:
Cechin et al. (2013) used MARS to identify and model price segments in the real estate market of Porto Alegre, Brazil, showcasing how MARS can help in understanding different market dynamics within a city.


Practical Implementation:

Real Estate Analytics Companies:
Companies like CoreLogic or Zillow might use advanced statistical techniques including MARS or similar methods for their Automated Valuation Models (AVMs). While they might not disclose specifics, their need for accurate and nuanced valuation models would make MARS a candidate technique.
Appraisal Districts or Tax Authorities:
Some local tax assessment offices or appraisal districts might experiment with or adopt MARS for property tax assessments where high accuracy is required, especially in regions with heterogeneous property characteristics or where traditional methods fail to capture complex market dynamics.
Custom Appraisal Services:
Specialized appraisal services that aim to provide more precise valuations for high-value properties or commercial real estate might employ MARS. These services could be catering to niche markets where understanding complex interactions between property features and value is crucial.


Indirect Evidence:

Software Tools:
The availability of MARS in software like Salford Systems' MARS, or its implementation in R via the earth package, suggests that there's a demand for such advanced statistical tools in sectors like real estate where data analysis is key.
Consulting and Analytics Services:
Consulting firms specializing in real estate analytics might use MARS models to provide insights to clients, helping in investment decisions, market analysis, or portfolio management.


Challenges and Considerations:

Data Quality: The effectiveness of MARS in real estate appraisal heavily depends on the quantity and quality of data. Missing or incorrect data can significantly impact the model's accuracy.
Complexity: The non-linear nature of MARS models can make them less intuitive for some appraisers accustomed to simpler linear models. However, this complexity can also be a strength in capturing real-world valuation nuances.
Adoption: While MARS might be used in research or by large companies, traditional methods like the sales comparison approach, cost approach, and income approach are still the bedrock of real estate appraisal due to regulatory standards and the need for transparency in valuation processes.


While direct, named instances might not be widely publicized due to proprietary reasons or because they're part of internal processes, the academic and theoretical groundwork exists, and the technology and data science capabilities are there for MARS to be a tool in real estate appraisal practices, particularly where high accuracy and nuanced understanding of property value determinants are needed."

 

[RCA]

And I posed this question to Grok-2. I like it's answer

ME: Do know of any applications of kriging to real estate valuation?


Grok-2: "Kriging, a geostatistical interpolation method, primarily used in spatial statistics for predicting values at unsampled points from data collected at sampled points, has indeed been applied to real estate valuation in various studies. Here are some applications and insights:

Applications of Kriging in Real Estate Valuation:

Spatial Interpolation of Property Prices:
Research by Dubin (1992): Used kriging to estimate house prices in a study titled "Spatial Autocorrelation and the Pricing of Residential Real Estate." The study highlighted how kriging could account for spatial dependencies in housing prices, which traditional hedonic models might overlook.
Mapping House Price Surfaces:
Kuntz & Helbich (2014): Their study, "Geostatistical mapping of real estate prices: an empirical comparison of kriging and cokriging," used kriging to create maps of absolute housing prices in Vienna. They compared ordinary kriging with cokriging, finding that including additional spatial and non-spatial variables in cokriging could enhance prediction accuracy.
Valuation for Taxation and Planning:
A study by Larson & Shui (2020) from the Federal Housing Finance Agency explored land valuation using public records and kriging in Maricopa County, Arizona. They constructed land values for each parcel, which has implications for land versus property taxation in urban areas, showing how kriging can be used for equitable tax assessments.
Premium or Discount Analysis:
Chica-Olmo & Cano-Guervos: In their work, they used regression-kriging to assess whether a house has a premium or discount relative to its neighbors, showcasing how kriging can be integrated with regression models to refine property valuation in a spatial context.
Predicting Rental Prices:
Seya & Shiroi (2019) compared kriging with deep neural networks for predicting apartment rent prices in Japan. They found that while neural networks could outperform in some scenarios, kriging was still valuable, especially when data was limited or when spatial relationships were crucial.


How Kriging Enhances Real Estate Valuation:

Accounting for Spatial Autocorrelation: Real estate values often exhibit spatial autocorrelation, where nearby properties influence each other's value. Kriging captures this spatial relationship, potentially providing more accurate valuations than non-spatial models.
Handling Spatial Heterogeneity: It can model the variability of property values across space, which is critical in urban areas where value can change dramatically over short distances.
Data Utilization: Kriging allows for the use of all available data points to estimate values at unsampled locations, making it useful in areas where sales data might be sparse.
Visual Representation: By creating surfaces or maps of property values, kriging can visually represent market trends, helping in understanding market dynamics or for urban planning purposes.


Considerations and Challenges:

Data Requirements: Kriging needs a good sample of spatially referenced data points. In real estate, this means having enough property transaction data with detailed location coordinates.
Model Complexity: While kriging provides detailed spatial predictions, the models can be complex to set up and interpret, requiring knowledge in geostatistics.
Computational Intensity: For large datasets, kriging can be computationally intensive, though various approximations and optimizations exist.
Implementation: The practical application of kriging in day-to-day real estate appraisal might be limited due to:
The need for specialized GIS or statistical software.
Preference for simpler, more transparent methods required by appraisal standards or regulations in some jurisdictions.


In summary, while kriging might not be a mainstream tool in everyday real estate appraisal due to its complexity and the need for substantial data, it is recognized in academic research and by some advanced analytics firms for its capability to offer nuanced insights into property valuation, particularly when spatial context is crucial."

 

[RCA]

And you see how backward traditional matched pairs analysis is, - just perhaps?