The selection of each technique aligns with standard machine learning data preprocessing practices.

City (name) is a nominal categorical feature. One-hot encoding is the appropriate method to convert these non-ordinal categories into a numerical format that an ML model can process, creating a separate binary feature for each city.

Type_year is a composite feature containing two distinct pieces of information: the home type and its construction year. Feature splitting is necessary to separate these into two independent, more useful features (type_of_home and year_built).

Size of the building is a continuous numerical feature. In real estate, features like size and price are often right-skewed. A Logarithmic transformation is used to handle this skewness, making the distribution more symmetric and helping to meet the assumptions of certain models like linear regression.

One-hot encoding: Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow (3rd ed.). O'Reilly Media, Inc. In Chapter 2, "Handling Text and Categorical Attributes" (pp. 71-73), the author explains and demonstrates the use of OneHotEncoder for nominal categorical features, which is the exact case for the City (name) feature.

Feature splitting: Stanford University. (n.d.). CS229: Machine Learning - Unsupervised Learning, K-means clustering. In the context of feature engineering, it's a common practice to decompose features. While not a formal named method, splitting a feature like Type_year into Type and Year is a fundamental feature engineering step to create more granular and meaningful inputs for the model. This is conceptually similar to extracting the month or day from a date feature.

Logarithmic transformation: University of California, Berkeley. (n.d.). Data 8: Foundations of Data Science, Chapter 15.2: Transformations. The course material explains that applying transformations like the logarithm is useful when dealing with skewed distributions or non-linear relationships, which is highly characteristic of housing size and price data. Applying a log transform can help linearize the relationship and stabilize variance.