From Record Cards to the Dynamics of Real Estate Transactions: Working with Automatically Extracted Information from Basel’s Historical Land Register, 1400-1700

From Record Cards to the Dynamics of Real Estate Transactions: Working with Automatically Extracted Information from Basel’s Historical Land Register, 1400-1700. Hitz, B., Prada Ziegler, I., & Vonwiller, A. In Baudry, J., Burkart, L., Joyeux-Prunel, B., Kurmann, E., Mähr, M., Natale, E., Sibille, C., & Twente, M., editors, August, 2024.

Paper abstract bibtex

This paper investigates the role of real estate in premodern Basel’s economy using the Historical Land Register. The register, with over 120,000 file cards, integrates various archival sources, revealing insights into real estate interests and their economic impact. We apply machine learning techniques to extract information, including entities and events, to analyze texts from handwritten records. We examine whether properties with higher interest burdens faced more frequent seizure procedures and which indicators may be of use to quantify interest burdens. This research aims to better understand the relationship between real estate interests and economic practices in historical Basel, while acknowledging and reflecting upon potential biases and limitations in the archival data and machine learning results and how it influences historical research practices. We find that while flawed and incomplete, we can make use of artificially created data to increase our understanding of these economic relationships by taking care which kind of information we rely on.

@inproceedings{hitzRecordCardsDynamics2024,
	title = {From {Record} {Cards} to the {Dynamics} of {Real} {Estate} {Transactions}: {Working} with {Automatically} {Extracted} {Information} from {Basel}’s {Historical} {Land} {Register}, 1400-1700},
	url = {https://digihistch24.github.io/submissions/462/},
	abstract = {This paper investigates the role of real estate in premodern Basel’s economy using the Historical Land Register. The register, with over 120,000 file cards, integrates various archival sources, revealing insights into real estate interests and their economic impact. We apply machine learning techniques to extract information, including entities and events, to analyze texts from handwritten records. We examine whether properties with higher interest burdens faced more frequent seizure procedures and which indicators may be of use to quantify interest burdens. This research aims to better understand the relationship between real estate interests and economic practices in historical Basel, while acknowledging and reflecting upon potential biases and limitations in the archival data and machine learning results and how it influences historical research practices. We find that while flawed and incomplete, we can make use of artificially created data to increase our understanding of these economic relationships by taking care which kind of information we rely on.},
	author = {Hitz, Benjamin and Prada Ziegler, Ismail and Vonwiller, Aline},
	editor = {Baudry, Jérôme and Burkart, Lucas and Joyeux-Prunel, Béatrice and Kurmann, Eliane and Mähr, Moritz and Natale, Enrico and Sibille, Christiane and Twente, Moritz},
	month = aug,
	year = {2024},
}

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