You can easily use xml (from the Python standard library) to convert to a pandas.DataFrame. Here's what I would do (when reading from a file replace xml_data with the name of your file or file object):

import pandas as pd
import xml.etree.ElementTree as ET
import io

def iter_docs(author):
    author_attr = author.attrib
    for doc in author.iter('document'):
        doc_dict = author_attr.copy()
        doc_dict.update(doc.attrib)
        doc_dict['data'] = doc.text
        yield doc_dict

xml_data = io.StringIO(u'''YOUR XML STRING HERE''')

etree = ET.parse(xml_data) #create an ElementTree object 
doc_df = pd.DataFrame(list(iter_docs(etree.getroot())))

If there are multiple authors in your original document or the root of your XML is not an author, then I would add the following generator:

def iter_author(etree):
    for author in etree.iter('author'):
        for row in iter_docs(author):
            yield row

and change doc_df = pd.DataFrame(list(iter_docs(etree.getroot()))) to doc_df = pd.DataFrame(list(iter_author(etree)))

Have a look at the ElementTree tutorial provided in the xml library documentation.

You can manually specify the namespace:

NAMESPACES = {'m': 'http://schemas.microsoft.com/ado/2007/08/dataservices/metadata'}
df = pd.read_xml(your_url, xpath='//m:properties', namespaces=NAMESPACES)

Output:

>>> df
       Id             NEW_DATE  BC_1MONTH  BC_3MONTH  BC_6MONTH  BC_1YEAR  BC_2YEAR  BC_3YEAR  BC_5YEAR  BC_7YEAR  BC_10YEAR  BC_20YEAR  BC_30YEAR  BC_30YEARDISPLAY  BC_2MONTH
0    7202  2018-10-09T00:00:00       2.17       2.25       2.46      2.65      2.88      2.98      3.05      3.15       3.21       3.30       3.37              3.37        NaN
1    7203  2018-10-10T00:00:00       2.18       2.27       2.45      2.67      2.88      2.97      3.05      3.15       3.22       3.33       3.39              3.39        NaN
2    7204  2018-10-11T00:00:00       2.14       2.27       2.44      2.66      2.85      2.94      3.00      3.09       3.14       3.25       3.32              3.32        NaN
3    7205  2018-10-12T00:00:00       2.14       2.28       2.44      2.66      2.85      2.93      3.00      3.09       3.15       3.25       3.32              3.32        NaN
4    7206  2018-10-15T00:00:00       2.17       2.31       2.47      2.67      2.85      2.94      3.01      3.10       3.16       3.27       3.34              3.34        NaN
..    ...                  ...        ...        ...        ...       ...       ...       ...       ...       ...        ...        ...        ...               ...        ...
295  7497  2019-12-16T00:00:00       1.57       1.57       1.58      1.54      1.65      1.67      1.72      1.82       1.89       2.17       2.30              2.30       1.57
296  7498  2019-12-17T00:00:00       1.56       1.56       1.58      1.53      1.63      1.66      1.71      1.82       1.89       2.18       2.31              2.31       1.56
297  7499  2019-12-18T00:00:00       1.56       1.56       1.58      1.54      1.63      1.67      1.74      1.86       1.92       2.22       2.35              2.35       1.57
298  7500  2019-12-19T00:00:00       1.54       1.57       1.57      1.52      1.62      1.65      1.73      1.84       1.92       2.21       2.35              2.35       1.58
299  7501  2019-12-20T00:00:00       1.57       1.58       1.58      1.52      1.63      1.67      1.73      1.84       1.92       2.21       2.34              2.34       1.59

[300 rows x 15 columns]

if the data is simple, like this, then you can do something like:

from lxml import objectify
xml = objectify.parse('Document1.xml')
root = xml.getroot()

bathrooms = [child.text for child in root['bathrooms'].getchildren()]
price = [child.text for child in root['price'].getchildren()]
property_id = [child.text for child in root['property_id'].getchildren()]

data = [bathrooms, price, property_id]
df = pd.DataFrame(data).T
df.columns = ['bathrooms', 'price', 'property_id']

    bathrooms   price      property_id
0   1.0        7020000.0    35237.0
1   3.0        10000000.0   32238.0
2   nan        4128000.0    44699.0

if it is more complex then a loop is better. You can do something like

from lxml import objectify
xml = objectify.parse('Document1.xml')
root = xml.getroot()

data=[]
for i in range(len(root.getchildren())):
    data.append([child.text for child in root.getchildren()[i].getchildren()])

df = pd.DataFrame(data).T
df.columns = ['bathrooms', 'price', 'property_id']